#  Canvil: Designerly Adaptation for LLM-Powered User Experiences

K. J. Kevin Feng  University of WashingtonSeattleUSA<EMAIL_ADDRESS>,  Q. Vera
Liao  Microsoft ResearchMontréalCanada<EMAIL_ADDRESS>,  Ziang Xiao
Johns Hopkins UniversityBaltimoreUSA<EMAIL_ADDRESS>,  Jennifer Wortman
Vaughan  Microsoft ResearchNew YorkUSA<EMAIL_ADDRESS>,  Amy X. Zhang
University of WashingtonSeattleUSA<EMAIL_ADDRESS>and  David W. McDonald
University of WashingtonSeattleUSA<EMAIL_ADDRESS>
(2024)

###### Abstract.

Advancements in large language models (LLMs) are poised to spark a
proliferation of LLM-powered user experiences. In product teams, designers are
often tasked with crafting user experiences that align with user needs. To
involve designers and leverage their user-centered perspectives to create
effective and responsible LLM-powered products, we introduce the practice of
designerly adaptation for engaging with LLMs as an adaptable design material.
We first identify key characteristics of designerly adaptation through a
formative study with designers experienced in designing for LLM-powered
products ($N=12$). These characteristics are to 1) have a low technical
barrier to entry, 2) leverage designers’ unique perspectives bridging users
and technology, and 3) encourage model tinkering. Based on this
characterization, we build Canvil111Canvil is available for public use at
https://www.figma.com/community/widget/1277396720888327660., a Figma widget
that operationalizes designerly adaptation. Canvil supports structured
authoring of system prompts to adapt LLM behavior, testing of adapted models
on diverse user inputs, and integration of model outputs into interface
designs. We use Canvil as a technology probe in a group-based design study (6
groups, $N=17$) to investigate the implications of integrating designerly
adaptation into design workflows. We find that designers are able to
iteratively tinker with different adaptation approaches and reason about
interface affordances to enhance end-user interaction with LLMs. Furthermore,
designers identified promising collaborative workflows for designerly
adaptation. Our work opens new avenues for collaborative processes and tools
that foreground designers’ user-centered expertise in the crafting and
deployment of LLM-powered user experiences.

large language models, user experience, design practice

††copyright: acmcopyright††journalyear: 2024††doi:
xxxxxxx.xxxxxxx††conference: arXiv; January; 2024††price: 15.00††isbn: xx-x-
xxx-xxxx-xxxxx††ccs: Human-centered computing Interaction design process and
methods††ccs: Human-centered computing Empirical studies in collaborative and
social computing

##  1\. Introduction

A paradigm shift is underway for integrating artificial intelligence (AI)
capabilities into everyday user-facing technologies. Large pre-trained AI
models, most notably large language models (LLMs), have versatile natural
language capabilities that unlock novel interactive techniques and interfaces
for more intuitive and customizable user experiences across a wide spectrum of
applications (Suh et al., 2023; Angert et al., 2023; Wu et al., 2022b; Parnin
et al., 2023). However, these promises also come with numerous concerns.
Integrating LLMs into a domain without careful consideration of the user
contexts surrounding model use and implementation of behavioral guardrails for
the model may result in user experiences that perpetuate societal biases
(Santurkar et al., 2023), threaten users’ sense of well-being (Shah, 2023;
Roose, 2023), or otherwise do harm (Selbst et al., 2019; Do et al., 2023).

As technology development practitioners, designers222We consider designers to
be anyone who has an active, hands-on role in designing the user experience of
a product and/or feature. This includes job titles such as UI and UX
designer/researcher, content designer, and product designer. are uniquely
positioned to mitigate these concerns (Feng and McDonald, 2023; Subramonyam et
al., 2021a; Liao et al., 2023; Yang, 2018; Yildirim et al., 2022). Designers’
work often involves aligning technological capabilities with user needs, such
that the technology addresses (or makes progress towards addressing) pain
points identified in user research (Feng and McDonald, 2023; Subramonyam et
al., 2022). Designers are trained in human-centered design methods that allow
them to understand users and usage contexts, prototype potential solutions
with relevant technology, and iterate on those solutions based on their
understanding of users or user feedback (Ball, 2005). Yet, prior work has
shown that designers face diverse challenges working with AI (Yang et al.,
2020; Subramonyam et al., 2021b). These challenges include procedural ones,
such as difficulties collaborating with the engineering teams training the
models (Subramonyam et al., 2022, 2021b), as well as instrumental ones, such
as lacking means to effectively work with the models (Feng and McDonald, 2023;
Yang, 2018; Subramonyam et al., 2021a). In efforts to address this,
researchers have situated AI as a design material to highlight exploring
material considerations—e.g., the technology’s capabilities, limitations, and
adaptability (Leonardi, 2012)—for designers to better understand and apply AI
in the context of their design problems (Yang, 2018; Dove et al., 2017; Feng
et al., 2023b). A good “designerly understanding” of AI can help designers
ideate on new AI-powered design ideas, mitigate AI’s varying impact for
different user scenarios, collaborate with design and non-design
practitioners, and reinforce user-centered perspectives amongst the team and
throughout the product development cycle (Liao et al., 2023; Wang et al.,
2023).

The advent of LLMs introduces new opportunities for engaging with AI as a
design material. First, adaptability emerges as a key materialistic property
of LLMs. LLMs are responsive to adaptation via fine-tuning (Dodge et al.,
2020; Hu et al., 2021; Ouyang et al., 2022b; Bai et al., 2022) and prompt-
based methods (OpenAI, 2023c; Wu et al., 2022a; OpenAI, 2023b). In fact, due
to resource-intensive training of LLMs, it has become a standard practice for
individual developers and development teams to adapt “base” LLMs from a small
handful of providers (e.g., Anthropic, Google, OpenAI) (Parnin et al., 2023)
for improved performance in domain-specific tasks. Moreover, natural language
interaction and adaptation democratizes AI experimentation for
practitioners—including designers—traditionally excluded from AI conversations
due to limitations in technical expertise (Liao et al., 2023; Petridis et al.,
2023b). Despite this, there has been limited exploration of designers’
interaction with—let alone adaptation of—LLMs in practice, with untapped
opportunities for designers to contribute to LLM-powered product development.

In this paper, we introduce the practice of designerly adaptation of LLMs
(henceforth “designerly adaptation”) to showcase new opportunities for
designers to harness LLMs as an adaptable design material. We first
characterize this new practice through a formative interview study with 12
designers experienced in designing for LLM-powered products and features. Our
study reveals that desirable characteristics of designerly adaptation include
execution via natural language prompt-based methods, leveraging designers’
user-centered perspectives and expertise, and encouraging iterative tinkering
with models.

While these desiderata align with those surfaced in prior work (Feng and
McDonald, 2023; Subramonyam et al., 2022; Yang et al., 2020), the practical
significance of satisfying them in design workflows remains unclear. To
investigate, we build Canvil, a technology probe in the form of a
Figma333Figma is a popular collaborative design tool: https://www.figma.com/.
widget that operationalizes designerly adaptation. Canvil enables designers to
adapt LLMs within their Figma canvases via system prompting (Microsoft, 2023;
OpenAI, 2023c) and integrate outputs from adapted models into their designs.
We use Canvil in a task-based design study with 6 groups of 17 designers total
to understand whether and how designerly adaptation can assist in crafting
LLM-powered user experiences. We find that through designerly adaptation,
designers steered LLM behavior with user needs, derived interface affordances
to enhance user interaction with LLMs, and recognized promises in
collaborative adaptation to share resources and knowledge with design and non-
design stakeholders. They were optimistic about integrating designerly
adaptation into their own workflows and noted procedural and ethical questions
to address in practice.

Equipped with findings from both studies, we propose a concrete workflow for
designerly adaptation to offer a tangible launchpad for researchers and
practitioners to further explore, critique, and iterate on this practice. We
end by underscoring the potential of tools for collaborative AI tinkering,
while reflecting on materiality’s impact on social practices within product
teams.

Concretely, our work makes the following contributions:

  * •

Insights from a formative study with designers experienced in crafting LLM-
powered user experiences, from which we assemble a characterization of
designerly adaptation—a new practice by which designers engage with LLMs as an
adaptable design material.

  * •

Canvil, a technology probe in the form of a Figma widget that operationalizes
designerly adaptation.

  * •

Insights from an empirical exploration of how designers engage in designerly
adaptation in a task-based design study.

  * •

A discussion of our work’s implications on (collaborative) design and beyond,
including a proposed workflow for designerly adaptation to orient future work.

##  2\. Related Work

To situate our work, we review prior literature in AI as a design material,
approaches to LLM adaptation, and tools for interactively working with AI
models.

###  2.1. AI as a Design Material

Robles and Wiberg argue that recent advances in computational technologies
bring about a “material turn”—a transformation within interaction design that
allows for the shared use of material metaphors (e.g., flexibility) across
physical and digital worlds (Robles and Wiberg, 2010). Indeed, prior works
have discussed AI as a design material (Dove et al., 2017; Yang, 2018; Yang et
al., 2020; Benjamin et al., 2021; Luciani et al., 2018; Liao et al., 2023;
Feng and McDonald, 2023; Feng et al., 2023c; Yildirim et al., 2022), and
highlighted why AI’s materiality can make it uniquely difficult to design with
(Yang et al., 2020; Subramonyam et al., 2021b; Liao et al., 2023; Dove et al.,
2017; Benjamin et al., 2021). AI is often treated as a black box to non-
technical stakeholders such as designers, making it challenging to tune user
interactions to often unpredictable and complex model behavior (Benjamin et
al., 2021; Yang, 2018). In addition, AI’s technical abstractions are often
divorced from concepts designers are familiar with (Subramonyam et al.,
2021b), and designers consequently struggle with creatively using or
manipulating the material to generate design solutions (Liao et al., 2023;
Feng and McDonald, 2023). AI models are also non-deterministic and fluid in
nature—they may evolve with new data or user input, and can be intentionally
steered towards desirable behaviors with choices of data, algorithm,
parameter, and so on (Yang et al., 2020). Without a concrete material
understanding to begin with, designers are unable to grasp the nature of these
uncertainties (Yang, 2018; Subramonyam et al., 2021a) or the opportunities to
shape the design materials for desirable UX (Liao et al., 2023). Yang et al.
(Yang et al., 2020) showed that these challenges of working with AI as a
design material persist through the entire double-diamond design process—from
identifying the right user problem to be solved by AI to designing the right
UX to solve the user problem.

Researchers and practitioners have developed processes and tools to alleviate
some of these challenges. ProtoAI (Subramonyam et al., 2021a) combines
exploration of models with UI prototyping, while advocating for designers’
active shaping of the AI design material (e.g., choosing models and setting
parameters) by user needs. Feng et al. (Feng and McDonald, 2023) found that
hands-on “fabrication” of the design material through a UI-based model
training tool bolstered understanding and connection between AI properties and
UX goals. fAIlurenotes (Moore et al., 2023) is a failure analysis tool for
computer vision models to support designers in understanding AI limitations
across user groups and scenarios. Other efforts include process models
(Subramonyam et al., 2021b) and “leaky abstractions” (Subramonyam et al.,
2022) that facilitate collaboration between designers and model developers,
and human-AI design guidelines (Apple, 2024; Google, 2024; Microsoft, 2024).

Recent advances in LLMs simultaneously alleviate and exacerbate some of the
aforementioned challenges. The barrier to tinkering with AI has significantly
lowered thanks to the use of natural language as a primary mode of interaction
and easily accessible tools such as ChatGPT. Concerns, however, have also
arisen over the lack of explainability and transparency in LLMs due to their
complex technical architectures (Liao and Vaughan, 2023). Yet, because of
LLMs’ powerful capabilities, there is significant interest in exploring their
integration into user-facing technologies (Pichai, 2023; Stallbaumer, 2023;
Parnin et al., 2023); as such, designers should be prepared to work with them
as a design material (Kulkarni et al., 2023). Despite this, we have yet to
understand designers’ current approaches and desiderata when crafting LLM-
powered user experiences. Our work contributes both empirical knowledge and
tooling via a technology probe to this space.

###  2.2. Adaptation of Large Language Models

A fundamental property of LLMs not present in their smaller predecessors is
the ease with which their behavior can be adapted (Brown et al., 2020; OpenAI,
2023b). While the breadth of LLMs’ out-of-the-box capabilities may seem
impressive, researchers have recognized the importance of adapting LLMs for
enhanced performance under specific domains and tasks (Dodge et al., 2020; Hu
et al., 2021; Wang et al., 2023; Wu et al., 2022b, a), and aligning model
behavior with human preferences and values (Ouyang et al., 2022b; Bai et al.,
2022; Feng et al., 2023a). Adaptation has thus been a topic of interest to
both the AI and HCI communities (Lee et al., 2022).

Adaptation may take on many forms. A pre-trained model may undergo fine-
tuning, a process by which additional layer(s) of the neural network are
trained on a task-specific dataset (Dodge et al., 2020). Popular approaches to
fine-tuning include instruction tuning (Wei et al., 2021; Ouyang et al.,
2022b), reinforcement learning with human feedback (RLHF) (Ouyang et al.,
2022b; Christiano et al., 2017), and direct preference optimization (DPO)
(Rafailov et al., 2023). More computationally efficient variants, such as low-
rank adaptation (LoRA) (Hu et al., 2021), have also garnered attention.

Adaptation can also occur without modifying the model itself, through system
prompting.444System prompting has also been referred to as “system messaging”
and “metaprompting” (Microsoft, 2023). We use “system prompting” in our paper
to align with terminology in prior academic work (Wei et al., 2023). Different
from one-off prompting, system prompting applies to all individual user inputs
for how the model should behave (e.g., “always respond in a concise manner”),
often targeting an application domain. OpenAI’s API provides a “System” field
to specify system prompts (OpenAI, 2023a, c), which have been shown to be
effective at modifying model behaviors (OpenAI, 2023b; Salewski et al., 2023;
Deshpande et al., 2023). For instance, a line of work explored instructing the
model to behave with a certain “persona” to elicit desirable or adversarial
model behaviors (Wei et al., 2023; Cheng et al., 2023; Salewski et al., 2023;
Wolf et al., 2023; Deshpande et al., 2023). However, system prompts can be
challenging to author (Zamfirescu-Pereira et al., 2023), and there is not yet
an established “gold standard” for prompt writing, system or otherwise.
Researchers and practitioners have attempted to derive useful prompting
formats based on empirical exploration; these include in-context learning
(i.e., by providing desired input-output examples) (Brown et al., 2020; Wu et
al., 2023; Zhao et al., 2021), chain-of-thought reasoning (Wei et al., 2022),
and instruction-following (Kaddour et al., 2023; Ouyang et al., 2022a).
Industry recommendations have also emerged for system prompts, encouraging the
specification of elements such as context specification (e.g., “You are Yoda
from Star Wars”), task definition (e.g., “You respond to every user input as
Yoda and assume the user is a Padawan”), and safety guardrails (e.g., “If the
user requests inappropriate or offensive responses, you must respectfully
decline with a wise Yoda saying”) (Microsoft, 2023).

In our work, we examine and support designerly adaptation of LLMs—whether and
how designers can contribute to model adaptation for user-facing products
through system prompting. We develop a characterization of the term through
our formative study, and then build a technology probe to operationalize it in
design practice. In building our probe, we draw from recommended best
practices for system prompting, but acknowledge that these practices may shift
over time and that our work reflects just one possible instantiation of these
practices.

###  2.3. Interactive Tools for Steering AI Behavior

Literature at the intersection of HCI and AI has introduced a wide range of
interactive techniques and tools to aid humans in training and adapting AI
models, ranging from ones supporting data scientists to perform data wrangling
(Görtler et al., 2022; Wang et al., 2022; Kandel et al., 2011; Liu et al.,
2020; Wongsuphasawat et al., 2015), model training and evaluation (Bäuerle et
al., 2022; Cabrera et al., 2023; Amershi et al., 2015; Ren et al., 2016;
Microsoft, 2024; Google, 2024; IBM, 2024), managing model iterations (Hohman
et al., 2020; Robertson et al., 2023), and so on, to those allowing for
“human-in-the-loop” paradigms at various stages of the model development
pipeline, including data annotation (Smith et al., 2018; Settles, 2012; Chao
et al., 2010; Monarch, 2021; Raghavan et al., 2006), output correction
(Branson et al., 2010; Bodén et al., 2021), integration testing (Chen et al.,
2022), and explainability (Lage et al., 2018; Qian et al., 2019; Nguyen and
Choo, 2021).

There has also been a long-standing interest in “democratizing AI” for domain
experts or practitioners without formal technical training to steer model
behaviors. Interactive machine learning (iML) (Fails and Olsen, 2003) is a
field responding to this interest by advocating for interactive and
incremental model steering through intuitive interfaces and tightly coupled
input-evaluation feedback loops. For instance, interfaces for transfer
learning (Mishra and Rzeszotarski, 2021) have enabled non-expert users to
transfer learned representations from a larger model to a domain-specific
task. Tools have also encouraged non-expert exploratory tinkering of AI models
through visual drag-and-drop UIs (Carney et al., 2020; Liner.ai, 2022;
Lobe.ai, 2021; Du et al., 2023). Teachable Machine (Carney et al., 2020) is
one such tool that allows users to train models for image, video, and audio
classification. Rapsai (Du et al., 2023) is a visual programming pipeline for
rapidly prototyping AI-powered multimedia experiences such as video editors.
In an era where formal knowledge about AI is limited to a small handful of
technical experts, these tools also serve to demystify AI for everyday users.

With the onset of language models, barriers to experimenting with AI have
lowered even further, paving the path for a new generation of interactive AI
tools. Sandbox environments such as OpenAI’s playground (OpenAI, 2024) require
no prerequisites besides a grasp of natural language to prompt the model and
steer model behavior. However, relying on unstructured natural language alone
can be daunting and ineffective. Many tools have risen to the challenge to
support prompt engineering with more structured tinkering. Prompt chaining is
one such approach (Wu et al., 2022b, a; Arawjo et al., 2023; Suh et al.,
2023), by which users can use a node-based visual editor to write prompts for
simple subtasks and assemble them to solve a larger, more complex task.
PromptMaker (Jiang et al., 2022) and MakerSuite (Huffman and Woodward, 2023)
allow the user to rapidly explore variable-infused prompts and few-shot
prompting, while ScatterShot (Wu et al., 2023) helps specifically with
curating few-shot prompting examples.

Despite advancements in tooling, support for designers to work with LLMs as an
adaptable design material remains limited. Domain-agnostic tools for tinkering
with models may not be well-integrated into design workflows, a primary
consideration for designers when deciding whether to adopt those tools (Feng
et al., 2023c). Tools that offer integration with design environments (e.g.,
PromptInfuser (Petridis et al., 2023b, a)) support prototyping with LLMs, but
not necessarily deeper adaptation of model behavior. Our work situates
interactive adaptation within familiar design environments and processes.

##  3\. Formative Study

Working with AI, especially LLMs, is an emerging practice in the field of UX
(Yang et al., 2020; Yildirim et al., 2023; Feng et al., 2023b; Subramonyam et
al., 2021b). We wanted to understand designers’ experiences working on LLM-
powered products and features amidst industry-wide shifts towards LLMs (Parnin
et al., 2023) as an initial step in our research. We hence conducted a
formative study where we interviewed designers about their awareness of,
involvement in, and desiderata around designing LLM-powered user experiences.

###  3.1. Method and Participants

We conducted 30-minute 1:1 virtual interviews with 12 designers at a large
international technology company, where LLM-powered features and products are
actively explored. All interviews were conducted in June and July 2023. At the
time of the study, all participants were working on products or services that
leveraged LLMs in some capacity; LLM application areas spanned conversational
search, domain-specific data question-answering (QA), recommendation, text
editing and generation, and creativity support tools. Participant details can
be found in Table 1 of Appendix A.

Our interviews were semi-structured and revolved around the following topics:

  * •

Awareness: to what extent are designers aware of LLMs’ capabilities,
limitations, and specifications in the context of their product(s)?

  * •

Involvement: to what extent are designers involved in discussions or
activities that shape where and how an LLM is used in their product(s)?

  * •

Desiderata: what do designers desire when crafting LLM-powered user
experiences, with regards to both processes and tools?

Each participant received a $25 USD gift card for their participation. All
interviews were recorded and transcribed. Our study was reviewed and approved
by the company’s internal IRB.

The first author performed an inductive qualitative analysis of the interview
data. This process started with an open coding round in which initial codes
were generated, followed by two subsequent rounds of axial coding in which
codes were synthesized and merged into higher-level themes. The codes and
themes were discussed with research team members at weekly meetings. Other
research team members also offered supporting and contrasting perspectives on
the codes by writing their own analytic memos, which were also discussed as a
team.

###  3.2. Findings

We present summaries of our major themes as findings below. While our research
did not focus specifically on model adaptation to start, we saw adaptation
emerge as a connecting thread across many of our participants’ experiences; as
such, we present our findings through the lens of adaptation. Additionally, we
use our findings to motivate and define designerly adaptation, culminating in
a concrete characterization of the term in Section 3.3.

####  3.2.1. Adaptation was seen as a central materialistic property of LLMs

Designers recognized that products delivering compelling, robust user
experiences were not powered by out-of-the-box “base” LLMs, but required
adaptation and, in some cases, grounding in other sources. P5 gave an example
where an out-of-the-box model inappropriately prompted the user for a riddle
in a workplace setting: “[The experience] is not quite right, cause it’s in
[workplace software] and it’s like, tell me a riddle. I’m at work!” Those
working in the domain of conversational search knew the model had to be
connected to a search engine to ensure information relevance and reliability,
so that they are “not just giving [the user] the chatbot” [P9].

The ability to steer an LLM with natural language enabled designers to
envision more flexible and adaptable AI-powered user experiences than working
with traditional AI models. Some were familiar with how lightweight adaptation
techniques, such as writing system prompts, can be used to specify model
behaviors based on desirable UX. P12 discussed an example of how this may work
in an entertainment system setting:

> “You might want to generate enthusiasm more, right? So the LLM that goes
> over there might have a UX layer that feels different. It should. I hope it
> does. The system prompt can say, remember, you’re a machine that wants to
> get users enthused.” [P12]

Participants recognized that the adaptability of LLMs allows their team to
more easily create multiple versions of LLM-powered features in one product.
P2 shared that they were already seeing multiple versions of the same model
customizing experiences within their product: “There are actually 3
incarnations of the same model, each with slightly different parameters […]
based on the prompt that users give, we select which of those 3 incarnations
we wanna use.” Participants also believed adapting the model’s ability or
behaviors for different user groups or contexts can help create more equitable
experiences. For example, P4 and P7 both stated a design goal for their
products (in conversational search and domain-specific question-answering,
respectively) is to accommodate non-technical users, which can be achieved if
the model can “tailor the language depending on technical ability, and
gradually introduce [users] to more complicated concepts and terminology.”
[P7]. Adaptation thus not only allows user experiences to more flexibly
accommodate existing user groups, but also to expand to serving new ones.

####  3.2.2. Designers were often not involved with adaptation, but desired
more involvement.

Even though designers found adaptation inextricably linked to UX
considerations, they were typically not involved in adaptation. Some were
unsure of how or by whom adaptation is performed and suspected that it was
engineers: “I would say [adaptation]’s probably something that was decided by
the engineering team […] prompt engineering is more so on the [technical] side
of things.” [P2]. Many knew for a fact that it was engineers and data
scientists who adapted the models and relied on them for information about
model behavior. This often got cumbersome quickly. P11 explained that in their
product, designers set character limits for LLM-generated summaries in the UI,
but were unable to directly specify and experiment with the model’s output
length. Instead, P11 relied on engineers to adapt on their behalf: “I was
asking what the difference in summary looks like with 50, 100, and 150
characters. Then [the engineers] would go back and test it and then they would
just share the results, like here’s what 50 characters looks like. Here’s what
100 looks like.” P12 experienced a similar procedure and stated that the
feedback loop can take as long as a couple weeks. Ideally, they wanted the
experimentation to happen “in real time.”

The combination of 1) adaptation and UX being closely intertwined, and 2)
designers’ exclusion from the adaptation process, led to a sentiment echoed by
the far majority of our participants: designers should have an active role in
model adaptation. Indeed, designers are uniquely positioned to contribute to
adaptation through their user-centered lens. For example, P4 shared that they
naturally gravitate to persona-based reasoning in their design process: “How
would I use this? How would my mom use this? Taking on [the perspective of]
different personas made sense to me.” This type of reasoning is critical for
adapting user experiences to accommodate diverse user needs and usage
contexts. Indeed, P10 caught their model’s inability to properly recognize
some acronyms users of their product might come across and devised a solution
with their team: “I found that [the model] doesn’t know what to do with the
acronyms so we floated the idea of having glossary of industry jargon and
acronyms [in the system prompt].”

Because of this, many participants were advocates for designers performing
adaptation, and considered UX goals as indispensable in this process. P4
shares: “Who better to involve in this process than people whose job it is to
think deeply about [the UX]? At the very least, system prompts shouldn’t be
written without an understanding of what the end UX goals are.” P9 agreed,
saying that designers can offer strategic contributions with user-centered
thinking: “It’s super important for designers to think through ideas and make
it intuitive for users and help come up with compelling [user] scenarios […] I
think design has a bigger opportunity to have a seat at the table strategy-
wise.” On a higher level, P12 pointed out that model adaptation can be a
contemporary extension of efforts around crafting product voice and tone,
which designers are already familiar with: “UX designers and content designers
are very attuned to and have pretty much owned the story around voice and tone
of products, and have for years and years.”

We see a broad consensus among participants that there is both demand and
opportunity for designers to engage in adaptation. We therefore propose that
designerly adaptation should leverage designers’ existing expertise and
workflows to enhance model behavior in user-centered ways.

####  3.2.3. Designers lack support to tinker with and adapt LLMs

Overall, participants felt limited by current tools and resources to tinker
with LLMs. These findings echo prior work that finds that the inability to
directly access the models and experiment with their capabilities and
limitations (i.e., “tinkering”) is a primary challenge in the design process
for AI-powered user experiences (Yang et al., 2020; Subramonyam et al., 2022).
Many participants emphasized the importance of “tinkering” to understand model
capabilities and limitations to inform design ideation and interface
prototyping. After some hands-on experience with the model, P2 said they could
much more clearly “see or gauge the power of the language model,” while P9
tinkered with a model they had early access to and recalled that “you can
identify some gaps [in capability] right off the bat.”

While some designers welcomed the easy access to ChatGPT or tried out the GPT
playground as a means to familiarize themselves with the “base” LLMs,
participants recognized the necessity of tinkering with the adapted models. To
gain access, designers either had to keep burdening the engineers or wait
until a test version of the product is launched. P3 shared that “The only way
we would have [to tinker] is using the [product] online and play around with
what [the team] did.” P10 expressed frustration at this workflow: “You want to
be able to play with [the model] yourself and understand what the user
experience is like. And I can’t play with it.”

Some designers took the initiative to seek out new tools to tinker with and
adapt models—currently, these tools are mostly limited to either programming
with APIs or playground interfaces with many technical developer settings.
However, usage of those tools did not persist. P1 tried various tools within
and outside of their company and did not find them to be designer-friendly:
“[The tools are] still kind of technical, a lot of [designers] don’t realize
how parameters work. Like how does temperature work?” Others, such as P4, did
not find the tools well-suited for their use case: “I haven’t seen anything
that feels like it makes it really easy to make small changes to a [system]
prompt and then run the same set of queries over it and see whether it makes a
difference.”

Taken together, we envision adaptation and tinkering must be tightly coupled
for designers to work with adapted LLMs within their design process. This
includes being able to easily create, test, and iterate on versions of
adaptation, and having this workflow be integrated into their design
environment to reduce friction and eliminate dependence on engineers.
Moreover, as noted in Section 2.2, some approaches to adaptation demand
technical skills designers typically do not acquire in their training, and
also do not fit within the tight feedback loops of the design process due to
high data and compute overheads. Thus, we consider prompt-based adaptation
methods to be most ideal for designerly adaptation. This broadens
participation for designers while still allowing for modification of many
aspects of model behavior.

###  3.3. Summary: A Characterization of Designerly Adaptation

We present three characteristics of designerly adaptation through several key
insights drawn from our formative study’s findings. First, not all forms of
adaptation can be considered as designerly adaptation. Designerly adaptation
should not demand a high degree of technical expertise in AI—instead,
designers should be capable of and prepared to conduct it with their current
skillset and training. One promising approach to designerly adaptation, then,
is to author system prompts with natural language. Second, designerly
adaptation should take advantage of designers’ unique expertise and
perspectives to inform model behavior. This may include incorporating user
research into the system prompts. Third, tinkering should have a major role in
designerly adaptation, encouraging tight feedback loops of creation, testing,
and iteration that align with feedback loops in the broader design process.

##  4\. Canvil: a Technology Probe for Designerly Adaptation

In light of our formative study, we introduce Canvil,555The name “Canvil” is a
portmanteau of “canvas” and “anvil.” We envision Canvil to be a metaphorical
anvil by which LLM behavior can be shaped within design canvases. a technology
probe for designerly adaptation in the form of a Figma widget. We first
outline our design goals, which we ground in our formative study’s findings.
We then walk through Canvil’s user interface and implementation.

###  4.1. Design Goals

Our design goals, informed primarily by insights from our formative study and
supported by our literature review, guided why and how we built Canvil. They
are as follows.

  * DG1:

Support designerly adaptation via system prompting.
Our formative study revealed that some designers were already experimenting
with system prompting in their design workflows (Sections 3.2.1 and 3.2.2).
System prompts are also an appropriate medium for architecting UX—they shape a
model’s behaviors across all or a specified group of user inputs. We also note
that among the diverse techniques for adaptation (see Section 2.2), system
prompting is conducted in natural language, making it accessible to designers,
many of whom lack a technical background.

  * DG2:

Leverage abstractions and environments designers are already familiar with.
Designers who want to be more involved in adaptation can feel unsupported due
to existing tools (e.g., calling APIs programmatically) not aligning with
their established workflows and mental models. Indeed, we saw in Section 3.2.3
that designers sought out new tools for adaptation but discarded them after a
while because they were too technical or did not match desired use cases.
Given that designers prefer to stay in one tool of their choice (e.g., Figma)
for most stages of the design process (Feng et al., 2023c), we hypothesize
that tools for designerly adaptation should be tightly integrated with design
tools and leverage common abstractions within those tools (e.g., layers,
components, frames) to better align with designers’ mental models.

  * DG3:

Allow for user research to inform model behavior.
When an LLM is integrated into a user-facing application, it becomes part of a
sociotechnical system embedded in the deployment context (Selbst et al.,
2019). Designers are well-positioned to understand this context and leverage
their understanding for LLM adaptation from working closely with users or user
research data (Section 3.2.2). Therefore, we invite designers to consider the
user and social factors surrounding the UX at hand in tandem with the
technical capabilities of the model. That is, we empower designers to adapt
model behavior with user research insights.

  * DG4:

Provide opportunities for collaboration.
UX is a highly collaborative practice (Feng et al., 2023c; Deng et al., 2023).
In our formative study, we saw that designers collaborated closely with not
only other designers, but also product managers, software engineers, data
scientists, and more. All these roles can contribute to adaptation. Moreover,
not all desired model behaviors can be realized with prompt-based adaptation
alone—for example, connecting an LLM to a knowledge base will likely require
collaboration with a developer or data scientist (Section 3.2.1). We therefore
design our system to afford collaboration with diverse technical and non-
technical stakeholders.

  * DG5:

Enable tightly coupled model adaptation and tinkering within the design
process.
Designers found model tinkering highly valuable in our formative study as it
allowed them to better understand LLMs as a design material—how they behave,
what they are capable of, and where their limitations are. This aligned with
prior work on designers working with AI models more generally (Subramonyam et
al., 2021a; Feng and McDonald, 2023). Yet, our formative study indicates that
designers currently had limited to no involvement in adaptation, nor access to
tinkering with adapted models. Even if some limited opportunities for
tinkering exist, they would require designers to leave their established
workflows to seize those opportunities. We thus aim to incorporate tinkering
and adaptation seamlessly into the design process.

###  4.2. Canvil as a Probe

We envision designerly adaptation as a new opportunity for designers to
contribute to model adaptation efforts. To investigate whether and how this
new practice may be included in existing design workflows, we designed Canvil
as a technology probe.

Commonly used in contextual research in HCI (Jörke et al., 2023; Hohman et
al., 2019), a technology probe is an artifact, typically in the form of a
functional prototype (Hutchinson et al., 2003), presented to the user “not to
capture what is so much as to inspire what might be” (Lury and Wakeford,
2012). That is, probes offer one instantiation of tooling and/or interaction
techniques for a domain to better understand phenomena within that domain.
Hutchinson et al. (Hutchinson et al., 2003) state that technology probes have
three goals: the social science goal of understanding users in a real-world
context, the engineering goal of field-testing the technology, and the design
goal of inspiring new technologies. We map these three goals onto our
objectives with Canvil:

  * •

Social science: understand how designers’ engagement with model adaptation
impacts their work on LLM-powered user experiences through a structured design
activity in Figma.

  * •

Engineering: develop and launch a Figma widget that connects to OpenAI’s LLMs
and allows them to be adapted via system prompting from within Figma.

  * •

Design: encourage reflection on designerly adaptation as a UX practice and
inform future tools to support it.

###  4.3. User Interface

The Canvil interface resembles an interactive card. The card itself is
separated into two areas: the Main Form and Playground Area. When a user
selects a Canvil, a property menu is invoked that can open up additional
panels for styling, response generation, and settings. Canvils can be freely
placed on and moved around the Figma canvas, allowing model tinkering to take
place in close proximity to relevant designs. Canvils can also interact
directly with designs by reading inputs from and writing model outputs to
their text layers. We detail each of Canvil’s features in this section and
connect them with our design goals.

Figure 1. An overview of the Canvil interface. A: A blank Canvil with its
property menu invoked. The property menu has options for styling (1), model
response generation (2), and model settings configuration (3). The Main Form
(4) contains text input fields for structured authoring of system prompts. The
Playground Area (5) allows users to quickly test inputs and view model
outputs. B: A Canvil with some copies styled with pre-set color options in the
property menu. Like any other native Figma object, Canvil allows for stateful
duplication. C: Canvil supports collaboration by default. Here, we see Figma
users collaboratively authoring a Canvil titled “Yoda impersonator.”

On the very left, a white card that resembles a form (the Main Form) with a
section at the bottom in light gray (the Playground Area). A dark gray
property menu is on top of the card, with 3 options: a color picker, Generate,
and Settings. In the middle, the same card is in front while cards of
different colors are fanned out behind it, while 6 color options are open in
the property menu. On the very right, a green card titled Yoda impersonator
with text populated in each of its fields is collaborative edited by multiple
cursors.

####  4.3.1. Main Form

The Main Form provides a means of authoring a system prompt (DG1) in a
structured manner via a multi-field form. Unlike prior systems that offer more
or less an open text field for system prompting (OpenAI, 2023a; Petridis et
al., 2023b, a), we chose to enforce structure because it provides mental
scaffolding to reason about the system prompt in a UX context from multiple
facets (e.g., with whom will the LLM interact, and how should the LLM meet
their goals?), thus providing more opportunities for designers to integrate
user context into the LLM (DG3). Additionally, designers can collaboratively
author system prompts (DG4) by each working on a particular field and
collectively deliberating afterwards. Breaking down the system prompt into
smaller units also allows for more fine-grained experimentation
(DG5)—designers can copy a Canvil and tweak a specific field to compare how
that change impacts model behavior.

Below are the fields, with a brief description of each, in the Main Form. The
field titles (in bold) are always visible on the interface, while the field
descriptions can be accessed by hovering over an info icon beside each field
title.

  * •

Model profile: High-level description of the model’s role, character, and
tone.

  * •

Audience setting: Persona or descriptions of user(s) who will interact with
the model.

  * •

Core instructions: Logical steps for the model to follow to accomplish its
tasks. Specify input/output format where applicable.

  * •

Guardrails: How the model should respond in sensitive or off-topic scenarios,
including any content filters.

  * •

Example inputs/outputs: Examples to demonstrate the intended model behavior.

We derived these fields from synthesizing system prompting guidelines offered
by technical tutorials (OpenAI, 2023c; Microsoft, 2023) as well as NLP
literature (Deshpande et al., 2023). We note that our fields are just one
possible way to structure a system prompt, and there may be some overlaps
between the fields. As discussed in Section 2.2, no “gold standard” currently
exists for prompt authoring, so we chose to follow the current recommended
guidelines when designing the Main Form.

####  4.3.2. Playground Area

Below the Main Form, we provide a Playground Area as an easy way to send user
inputs to the model and test model responses (DG5) within Canvil. The designer
may test on the same Canvil multiple times or duplicate one with its entire
state and test different tweaked versions (Fig. 1B). Designers are likely to
find stateful duplication intuitive as it is available on all native objects
in the Figma canvas (DG2).

####  4.3.3. The Generate Panel

To generate a response from a model adapted with the system prompt authored on
the Main Form, the user selects the “Generate” option from Canvil’s property
menu, which takes them to the Generate Panel. The panel has two modes:
Playground and Design. Both modes contain an option to copy the raw prompt to
one’s clipboard so it can be tested in a separate environment (e.g., a Python
notebook during collaboration with a data scientist), if desired.

Playground Mode. The Playground Mode (Fig. 2A) is invoked when the user
selects “Using playground” from the dropdown on the Generate Panel. This mode
instructs Canvil to read user input from the Playground Area and write its
response back to the Model Response area. This mode is the default mode in
Canvil.

Design Mode. The Design Mode (Fig. 2B) is invoked when selecting “Using
design” from the dropdown. In this mode, Canvil navigates the design layers on
the user’s Figma canvas, reading text inputs from a specified layer(s) from
those designs, and writing model responses to a specified layer(s). This mode
leverages the hierarchical layer structure of Figma designs (DG2) and
implements the “input-output” LLM-interaction proposed by Petridis et al.
(Petridis et al., 2023a).

The user can directly select a layer from their Figma canvas and use the “Set
read layer(s)” button to bind it as Canvil’s input. This allows Canvil to
retrieve and use the text from the bound layer as user input. If the user has
inputs from matching layers666We consider two layers as “matching” if they
both have the same name and are of the same Figma layer type. Matching layers
are common in designs with related information split up across different UI
components. For example, a chat UI may have many matching layers that
collectively contain one user’s dialogue across multiple conversation rounds.
from the same screen, they can check the option to include them as input, and
Canvil will read from those layers accordingly. Similarly, the user can select
a layer to which the model writes its response. However, when implementing the
checkbox for writing to multiple layers, we realized that writing the same
output to multiple locations on the same screen is rarely meaningful for
designers. As such, we designed the write layer checkbox to search for
matching layers across screens for simultaneous updates in multiple screens
along a user flow, ensuring content consistency within a particular matching
layer.

####  4.3.4. The Settings Panel

The Settings panel (Fig. 2C), accessible through the “Settings” option from
Canvil’s property menu, contains some basic model settings for the user to
configure, along with a field for the user to enter their OpenAI API key
required for response generation. We distilled the list of settings in the
OpenAI API to four (model selection, temperature777We rename “temperature” to
“creative randomness” to provide a more descriptive name for those not as
familiar with LLMs (confusion over the temperature parameter was raised in our
formative study—see Section 3.2.3)., maximum generation length, stop words)
that may be useful to non-AI experts such as designers. The “Update Settings”
button saves the settings to Canvil’s state, which is preserved when the
Canvil is duplicated.

Figure 2. The “Generate” and “Settings” options in a Canvil’s property menu
can lead to three panels that appear alongside the Canvil itself. A:
Playground Mode for response generation, where the input is read from and
written to the Canvil’s Playground Area. B: Design Mode for response
generation, where the input is read from and written to design(s) on the Figma
canvas. C: Settings for selecting and configuring the LLM.

From left to right: a Canvil with a property menu above it; a panel with an
option to generate a model response after reading the playground input; a
panel with an option to generate a model response after setting design layers
to read the input from and write the output to; a panel with a model selector
and model settings.

###  4.4. Implementation

Canvil is a Figma widget888https://www.figma.com/widget-docs/ implemented in
TypeScript using the Figma API.999https://www.figma.com/developers/api A Figma
widget differs from a Figma plugin101010https://www.figma.com/plugin-docs/ by
its collaborative nature. A widget is available to all users of the canvas
that it is placed in, and maintains a common state that supports multi-user
editing by default. Users can interact with widgets just like any other native
object on the Figma canvas, including moving, duplicating, and styling. In
contrast, Plugins are local to an individual user. The collaborative
affordances of widgets align well with DG4, so we implemented Canvil as a
widget.

Upon the user selecting ”Generate,” Canvil prepares a system prompt using the
text written on the main form. Each field in the form is converted to markdown
format and is packaged up in ChatLM111111https://learn.microsoft.com/en-
us/azure/ai-services/openai/how-to/chatgpt for added prompt parsability. Our
prompt template is available in Appendix B. The populated template is sent as
a system prompt to an OpenAI Chat API endpoint with settings specified in
Canvil’s settings panel. If generation was triggered in Playground Mode, any
text in the Playground Area will be sent as user input to the model. If
generation was triggered in Design Mode, Canvil searches for the read layer(s)
specified in Design mode on the user’s current Figma canvas, retrieves the
text within those layers, and sends them off to the API endpoint as user
input.

Canvil is compatible with both the Figma design editor and the FigJam
whiteboarding tool. All features have the same behavior in both environments,
except that the Design Mode for generation is not available in FigJam because
of FigJam’s inability to access and edit design elements.

##  5\. Design Study

With Canvil as a technology probe, we conducted a task-based design study with
17 participants organized into 6 groups. The study was conducted on a per-
group basis; each session was 90 minutes in length. Our study investigated the
following research questions:

  * RQ1.

Implications of adaptation: How does engagement with designerly adaptation
impact design workflows and outputs in the context of our study?

  * RQ2.

Adaptation at large: What are designers’ attitudes toward designerly
adaptation beyond the context of our study?

###  5.1. Participants

We recruited our participants from two channels. First, we distributed study
invites to designers via email, professional interest groups, and word of
mouth at a large technology company. Some of these invitees took part in our
formative study. We also snowball sampled by asking our invitees to refer us
to other designers they work with who may also be interested in participating.
From this channel, we only selected designers with prior experience working on
LLM-powered products and features. Second, we sent study invites to a Slack
workspace for HCI and design maintained by a large, public academic
institution in the United States. The population in the Slack consists
primarily of design students and early-career designers. From this channel, we
only selected those without any experience working on LLM-powered products and
features. Our goal of recruiting from these two channels was to capture
potential disparities that may arise in our findings hinging on prior
experience working with LLMs.

Invitees first filled out a screening form with information on demographics,
professional background, prior exposure to LLMs, and group member preferences.
We then organized all candidates from the technology company into groups based
on member preferences and availabilities, and did the same for candidates from
the academic institution. We left recruitment open for both channels as we
conducted the studies and stopped when we reached data saturation. In the end,
we had 6 groups (3 groups from each channel) with 17 participants (8 from the
technology company and 9 from the academic institution). Five groups were of
size 3 and one of size 2. All participants were based in the United States.
Participant and group details can be found in Table 2 in Appendix C.

All study sessions were recorded and transcribed. We sent each participant a
$75 USD gift card as an honorarium after the study. This study was approved by
the Institutional Review Boards of all organizations involved.

###  5.2. Study Design

Our task-based study design was motivated by contextual inquiry (Beyer and
Holtzblatt, 1999) due to its ability to elicit rich information about
participants’ work practices and processes. We conducted our study virtually
over videoconferencing software and Figma. We opted for a group study design
to better emulate the team-based environment in which many designers now work
(Feng et al., 2023c). A group study may also help uncover more insights about
enabling collaboration (DG4) than prior individual design studies about AI-
powered user experiences (Petridis et al., 2023b; Subramonyam et al., 2021a;
Feng and McDonald, 2023; Feng et al., 2023b).

####  5.2.1. Setup

The following instructions for the study’s design task were provided to all
groups:

> You are all designers on a product called Feasto. Feasto aims to increase
> users’ enjoyment of food. The Feasto team is scoping out a new feature
> called the 3-course meal planner, which allows users to get suggestions for
> 3-course menus they can cook by simply listing out some ingredients they
> have. The team agrees that large language models (LLMs) are a promising
> technology to power this new feature. Your task is to design the UX for the
> area in which users will see and interact with the suggested menus.

We assembled these instructions cognizant of the fact that this was a time-
constrained study, and we wanted participants to specifically focus on the
area of the interface where users interact with an LLM. To reduce design
overhead unrelated to our research questions, we provided starter UIs for
participants to work with, along with basic UI components such as text,
buttons, and sticky notes (Fig. 3B). If participants had an idea but did not
have time to execute it, we encouraged them to describe it on a sticky note
next to their designs.

To better probe research-informed model adaptation—that is, adaptation across
varying user contexts informed by user research (DG3)—we created descriptions
for three hypothetical user groups of this new Feasto feature based on users’
geographic region: west coast of the U.S., Turkey, and India. These
descriptions are not meant to act as user personas, but rather high-level
sketches of the customs and preferences that may be prevalent in the region.
We crafted the user group descriptions to vary along three key dimensions
(assuming everyone used Feasto in English): dietary restrictions, access to
ingredients, and menu style preferences. Definitions for these dimensions,
along with the full user group descriptions, are available in Appendix D.

We provided one blank starter UI with some example user inputs and one blank
Canvil per user group. Participants were invited to vary the user experience
between user groups as much or as little as they saw fit. We also encouraged
participants to use Canvil to adapt an LLM and test its behavior as they
designed.

A Figma file for each study group contained all the materials described above.
Within the file, we created separate canvases for each participant to act as
individual workspaces, each with its own copy of the materials (Fig. 3). We
also had a shared canvas for introductions and instructions before the task
and collectively debriefing afterwards.

Figure 3. The setup for an individual participant’s canvas in our study’s
Figma file. A: Informational packet containing descriptions of three user
groups residing in North America (primarily west coast of the U.S.), the
Middle East (primarily Turkey), and Asia (primarily India), respectively. B:
Starter UIs for Feasto’s 3-course meal planner with example user inputs to
lower the barrier for testing model responses, along with basic UI elements
such as text and buttons. C: Blank Canvils for participants to adapt LLMs.

A Figma canvas with three sections. The top section has a 4-page document
containing text descriptions of three user groups for participants to work
with during the study. The middle section has starter UIs and UI elements for
Feasto’s 3-course meal planner feature. The bottom section has three blank
Canvils for participants to adapt LLMs.

####  5.2.2. Procedure

Our 90-minute study was divided as follows.

Introduction (20 minutes): First, all participants introduced themselves to
others in the group. The study facilitator then gave a demo of Canvil,
covering all features described in Section 4.3 using a pre-filled Canvil. The
facilitator also described the design task and answered any clarifying
questions from participants.

Design task (40 minutes): Participants spent 40 minutes on the design task and
were asked to consider at least two of the three user groups provided.
Participants were encouraged to spend 10 minutes authoring a Canvil and
another 10 minutes on UI design per user group. Some participants who had
remaining time designed for all three user groups.

Group interview (30 minutes): Participants first filled out a brief usability
questionnaire about Canvil before gathering in the shared page of the Figma
file. They were asked to copy their Canvils into the shared space and also
their designs (if desired) to share with the group. The facilitator then led a
semi-structured interview that asked participants to reflect on their
experience adapting models with Canvil, Canvil’s collaborative capabilities,
and how they see adaptation fitting in with their own design practice. The
facilitator ensured that each participant had ample opportunity to express
their thoughts in the group setting, and also encouraged dialogue between
participants.

###  5.3. Data Analysis

We conducted a qualitative analysis of transcriptions and Figma canvases
(including authored Canvils), as well as a quantitative analysis of feedback
from Canvil’s usability questionnaire.

For our qualitative analysis, the first author took a hybrid inductive-
deductive approach to coding the group interview portion of the transcriptions
from the study. This process started with an open coding round in which high-
level themes were generated, followed by subsequent rounds of thematic
analysis via affinity diagramming in which themes were broken down into sub-
themes. This approach was taken because new subtleties and complexities
emerged from our initial codes as coding progressed due to the diverse
approaches observed in our study as well as participants’ group discussion
dynamics. The codes and themes were discussed and iterated on with research
team members at weekly meetings. Additionally, whenever participants made
references to content within their Figma canvases (e.g., their designs and/or
Canvils), the first author took screenshots of those references and linked
them to transcript dialogue. Summary memos were then written for our high-
level codes and presented alongside relevant screenshots.

Canvil’s usability questionnaire followed the standard template for the System
Usability Scale (SUS) (usability.gov, 2022; Brooke, 1995) and consisted of 10
questions, each with five response options for respondents from Strongly agree
to Strongly disagree. We computed a SUS score using methods outlined by Brooke
(Brooke, 1995) for each participant and subsequently computed a mean score and
standard deviation.

##  6\. Results

Our quantitative analysis showed that Canvil had a mean SUS score of 69.94
(std = 12.18), meaning its usability was “above average” (usability.gov,
2022). In this section, however, we focus on the qualitative insights on
designerly adaptation from our design study. We specifically showcase the
implications of satisfying Canvil’s design goals (Section 4.1) on design
workflows and outputs. Our results from designers without prior experience
working with LLMs did not differ noticeably from those with experience, except
that the latter drew more connections between adaptation and their past model
tinkering workflows. We present text that participants wrote in Canvil as
purple and italicized.

###  6.1. Authoring System Prompts as Part of Design Workflows

####  6.1.1. Research-Informed System Prompting as a Design Activity (DG1,
DG3)

When authoring system prompts in Canvil (DG1), designers frequently referenced
the provided user research documents. It is through this process that they
embedded user context—e.g., varying lifestyles, customs, preferences—into
system prompts (DG3). For example, in P2’s Canvil for the American user group,
they wrote under Audience Setting that the model is serving users who like
diverse cuisine but care about the environment and sustainability and are
health-conscious. Designers also adjusted their prompts between user groups in
attempts to create more customized and meaningful user experiences. In the
Canvil for the user group from India, P4 wrote that The model should use
Indian terminologies to describe the recipe. Eg: eggplant is brinjal in India.
P5 experimented with model variations within one user group, choosing
distinctly different personalities for each model. For their Indian user
group, they populated one Canvil’s Model Profile field with The model is a
head chef of a 5 star restaurant situated in New Delhi. It is a busy day for
the restaurant and the chef is low on time. and another with The model is a
mother who is helping her son cook quick meals in hostel. In the end, they
noted that “the models did very well” in taking on these different profiles.

In particular, designers used the Guardrails field in Canvil to address
potential violations of users’ dietary preferences and restrictions, along
with model misuse. For example, P12 used information provided on the Turkish
user group to create the following guardrail: The model should not include
recipes with pork or alcohol and must respect the fasting period of Ramadan by
suggesting suitable pre-dawn and post-dusk meals. A few participants noted
that some user groups may require more strict model guardrails than others.
P15 identified a subtle but key requirement between two user groups while
authoring their Canvils:

> “I wanted to highlight that the model could take many more liberties with
> the recipes that it was giving [to the Turkish user group], but it couldn’t
> take more liberties with the ingredients. It could be very loose with: try
> this, try this with this, but never like crossing the boundary of the
> dietary restrictions. Whereas with the [American] one, it’ll take it into
> account, but it’s not gonna mar their religious practice.” [P15]

Designers sought to address user needs through combined efforts in prompt
authoring and UI design. For example, P15 provided alternatives via buttons to
support more flexible user input of ingredients going into the model. P8
described their UI design in response to observing the limits of what can be
achieved via writing system prompts: “Sometimes even in the instructions that
I gave to Canvil, it wasn’t really reflecting that [desired] granularity until
I pushed it further. In my design I ended up putting a little textbox area
where people can specify how detailed they want the instructions.” Fig. 4
contains some examples of designers’ UIs from our study.

In short, the structure provided by prompt authoring interface of Canvil is
compatible with designers’ reasoning about user needs and preferences based on
user research. Canvil supports this reasoning process to inform both the
system prompts and UI design in an integrated fashion, and makes authoring
system prompts a natural part of design activities.

Figure 4. Examples of UIs designers created for Feasto’s 3-course meal planner
during the study.

6 UIs showing different approaches designers took to designing the UI for
Feasto’s 3-course meal planner, including simply displaying the output, chat
interfaces, and additional buttons.

####  6.1.2. Tinkering Allowed for Deeper Reasoning of User-LLM Interaction
(DG2, DG5)

Canvil’s integration in the design environment (DG2) allowed designers to
consider model behavior and user interaction simultaneously. Many designers
iterated multiple times on their system prompts (DG5) until they were
satisfied about the adapted model. P3 reformatted the output to better suit
their existing UI: “the first run was a paragraph that I felt like was really
hard to read, so I added some core instructions saying give me a numbered
list.” P13 shared that their model was repeating the same ingredients for each
course, so they “tweaked the level of detail and the recipes to add more
variety.” P11 discovered that, in general, “stating [instructions] in the
positive instead of the negative” yielded better results for their envisioned
experience.

Designers’ tinkering with the adapted model not only helped them iterate on
their system prompts, but also allowed them to more deeply reason about user
interactions with LLMs and reflect on the whys of UI design. For P14,
grappling with the Example Inputs and Outputs field in Canvil prompted
reasoning about the optimal interaction pattern to design with:

> “The example inputs and outputs were a little bit hard for me to think
> about, cause I was wondering for the output, is it just the recipe? Or would
> it be more of a conversation, like oh, do you have dietary restrictions? Do
> you have any XYZ preferences? Like there’s some logic that goes into asking
> those questions and I was wondering which one should I think about first
> versus later.” [P14]

Tinkering with the adapted model often led to evolving of or experimenting
with UI ideas. P17 considered different interaction patterns for their
interface—a chat-based interface versus a form-filling GUI—after discovering
how sensitive LLMs can be to prompts. P4 mentioned that they started off with
“a specific user base in mind and I wanted a specific tone,” but the design of
the UI became clearer after they saw an ideal output after multiple
iterations: “My design direction changed when I saw the output. I iterated
multiple times and what I like the best about this [response] was that it was
short and [ideal] for scrolling in a screen. I tried that out [in my new UI]
and it worked pretty well.” Examples of quoted participants’ designs can be
found in Fig. 4.

Interestingly, a few designers wanted a degree of separation between UI work
and model tinkering. P1 shared that they found it “a bit hard to juggle
between Canvil and [the UI] at the same time”, especially when the output is
incompatible with their design settings: “What if the text is really long and
then I have to play with auto layout?” Therefore, P1 preferred first tinkering
with the model using the Playground Mode. P8 also agreed that having Canvil
inject outputs directly into text boxes can be “a little scary […] people
don’t wanna actually commit [responses] to text boxes sometimes.” Past work
considers integration between models and UI designs as desirable (Petridis et
al., 2023b, a; Subramonyam et al., 2021a). Our results encourage providing
designers with more choices and control over such integration.

###  6.2. Designers Envisioned Collaborative Workflows with Designerly
Adaptation

####  6.2.1. Collaborative Uses of Canvil in Design Teams (DG4)

The design task in our study was an individual activity because we expected
collaborative efforts to begin after designers had made some individual
attempts at adaptation and designed with the adapted model (Feng et al.,
2023c). Indeed, when reflecting on Canvil’s collaborative capabilities (DG4),
designers thought the workflow matched their own experiences. P8 did not
consider a “divide and conquer” authoring approach—where each team member is
responsible for one Canvil field—as necessary unless the team was under a
tight time constraint, and instead saw more potential in a “tinker and unite”
approach: “Since it doesn’t take too much time to generate a response, I feel
like I would lean towards, let’s all try different things and see how we can
use this tool to get the best response.” P8 added that in order for this
approach to work, some guidelines should be set for collaborators “so that all
of our responses are consistent, like everyone has the same Audience Setting
and [uses] the same kind of wording.” P4 agreed and suggested initial
brainstorming sessions for collaborators so that they could “find a way to
come to common grounds for all these [fields].” P6, after hearing P4’s
suggestion, noted that using Canvil within FigJam may be useful “for ideation
or brainstorming” before bringing more polished Canvils into Figma.

On the other hand, some saw potential in the “divide and conquer” approach,
especially when expertise in a design team may be distributed. P7, who
specialized more in visual design, said that they were “struggling a little
bit with like how to prioritize all the content, and I feel like that would be
specialties a content designer would bring in.” P8, who initially preferred to
“tinker and unite,” agreed that it would be valuable to also leverage
distributed expertise: “In a design team, there might be someone who knows a
user persona the best, and they can be assigned to Audience Setting. Then you
have content designers on maybe example inputs and outputs.”

####  6.2.2. Collaboration and Knowledge Sharing Afforded by Canvil (DG2, DG4)

Many designers commented on how Canvil, as a widget integrated into Figma’s
design environment (DG2), afforded collaboration (DG4). P5 appreciated that
they could leverage Figma’s built-in collaboration features: “If I see my
fellow designer’s Canvil, and if I want to change something then and there, I
can drop a comment on it just like a normal component in Figma, which we’ve
been doing in our everyday design work.” P10 shared that their team relies on
a spreadsheet to keep track of system prompt iterations and working in a
canvas environment would be a significant improvement.

In particular, participants saw potential for more effective knowledge sharing
about adaptation with Canvil. P10 commented that: “You could have a master
Canvil and then you could make copies, and [others] can then do their own
interpretations.” P11 agreed and added that seeing iterations on a canvas can
help find inspiration in others’ work: “I think anytime you line up different
iterations together, you notice: ohh that person, did you know they had that
approach? That’s a good idea. And I’m gonna try that over here. I think it
really is an aid to experimentation.” We observed other instances of knowledge
sharing as well when designers viewed others’ Canvils. For example, P4 noted
that they were inclined to consider more dimensions for their system prompt
after seeing P6’s work: “[looking at P6’s Canvil] made me thinking about
timers and Hindi slang.” Some also realized new capabilities of LLMs by
looking at others’ prompts and outputs—P1 shared that “I didn’t realize at
first that you can actually make the [LLM] generate multiple recipes, just
like what P2 did.” P1’s groupmates (P2 and P3) were also intrigued when they
saw that P1 had used one Canvil to generate example inputs for another Canvil
to use. These social and collaborative affordances differentiate Canvil from
prior systems for empowering designers to tinker with AI (Subramonyam et al.,
2021a; Petridis et al., 2023b; Carney et al., 2020).

####  6.2.3. Collaborating With Non-Design Stakeholders via Canvil (DG1, DG4)

With system prompt authoring as the primary interaction, Canvil (DG1) ensures
easy authorship and sharing within a team (DG4), even across domain
boundaries. Many designers thought that Canvil was simple and intuitive enough
to be used by not just designers. Consequently, P2 saw Canvil as a useful
boundary object (Star and Griesemer, 1989): “I can see from a product
manager’s perspective that they would love to play around with the prompts,
and it would probably help the designer explain their design choices. I feel
they would find more common ground because they both used a similar tool.” P6
agreed that product managers (PMs) have perspectives and expertise to
contribute to prompt authoring. Designers also saw potential for deeper
collaboration with data scientists and other technical stakeholders. P11
commented that the “Copy raw prompt to clipboard” button in Generate Panel
(Section 4.3.3) can allow for direct handoff of system prompts: “[The button]
is a way of exporting that so a data scientist can come along and say sure,
let me plug that into code.” Even if designers were unable to achieve the
desired model behavior themselves, P17 thought Canvil was helpful in
specifying desired changes: “If I want the model to respond in this way versus
that way, just having something tangible to show engineering partners where
the tweak would need to be, would be helpful. And I think [that’s] obviously
easier with Canvil.” P15 believed that rise of natural language as a shared
representation can allow for more transparent and balanced information flow
between disciplinary boundaries:

> “I think on the designer side, there is apprehension about [AI] being in
> different skill set. The [technical] teams just don’t really embrace
> designers, cause this is ‘their’ skill set. They’re gonna do it quicker and
> better, and that’s probably true in almost all circumstances except natural
> language. And except when it comes to an intuitive experience. So it’s about
> making the teams meet each other as opposed to one team being more reticent
> than the other.” [P15]

Designers also acknowledged that designerly adaptation may only be one piece
of the larger puzzle to steer an LLM’s behavior and integrate it into a
product. Some desired changes, such as factual grounding and connecting to
external knowledge bases, may still need to be left to technical teams. P12
even saw factual grounding as a prerequisite to designerly adaptation:
“because everything hinges on you being able to get the model specificity
correct.” Both P12 and P14 wanted Canvil to be able to connect to other models
built and maintained by their own teams, as the behavior of unadapted OpenAI
models may be too generic to use as an effective design material. P14
comments: “I think linking to the vanilla ChatGPT model isn’t specific enough.
If there is a way to somehow have Canvil link to the devs’ [models] while I’m
designing and I’m able to see what type of experience and what type of
response our users are getting, I think that would be super helpful.”

While we currently offer only three OpenAI models to use with Canvil
(GPT-3.5-Turbo, GPT-3.5-Turbo-16k, GPT-4), more may be added with lightweight
engineering as the widget is communicating with the model via API endpoints.
As such, we can envision future support for more model families, including
teams’ custom models.

###  6.3. Designerly Adaptation at Large

####  6.3.1. Integrating Adaptation into Design Practice

Designers saw great value in engaging with adaptation and saw direct paths to
application in their own design practice beyond the study. P10, who has
extensive experience working on AI-powered products deployed to users
worldwide, said that adaptation has traditionally been difficult: “We had
teams of people training models and nudging the technology to align with [user
personas].” Having experienced adaptation of LLMs via Canvil, they shared that
“I can see this being something that would enable [adaptation] to actually
happen in a way that’s much more practical than taking Python classes, which
I’ve done.” For P13, who works on a product with enterprise and consumer
versions, adaptation can help set more detailed product requirements where
necessary: “I could see [adaptation] being really helpful, being able to
prototype and build separate generative models for enterprise customers and
then to work more closely with them to tune and add requirements.” P16
considered adaptation to be a useful exercise for user empathy: “for [Audience
Setting] I just tried to put myself in the mind of someone who’s using this
and say, I’m really good at following recipes, but have a variety of dietary
preferences and restrictions.”

Some participants pointed out that designers should be prepared to carefully
navigate challenges of adaptation that may arise in practice. P11 reflected on
the balance between specificity through adaptation and breadth of audience: “I
wanted to avoid bias in these [models]. There’s that balance between, having
the profile of the model be specific enough so that it’s relatable […] and
making technology for everybody. If you try to make it too generic, it’s just
gonna be bland. So we have to be careful.” P15 pointed out that adaptation may
not account for all user needs and preferences: “in terms of the normative
sense, it would be fantastic. But feasibly though, there are so many different
things that you need to take into account, like even length of language.”
These complexities highlight the broader social and ethical factors
surrounding designerly adaptation to confront as designers integrate it into
their own practice.

####  6.3.2. Designerly vs. End-User Adaptation

Prior work found that designers were naturally inclined to provide end-users
with more controls for customization of their AI-powered user experiences
(Feng and McDonald, 2023). Some designers also did the same in our study and
reflected on the interplay between designerly and end-user adaptation. P15,
who discussed the complex factors of adaptation in Section 6.3.1, said they
envisioned a more feasible approach to be designers delegating adaptation to
end-users past a certain point: “If we could put in a Venn diagram of all of
the different needs from all over the world, seeing what the more central
options are, the really interesting piece here is having users be able to make
that [adaptation] decision themselves.” P10, after interacting with Canvil,
started thinking about similar tools for end-user adaptation: “I thought
[Canvil] could be a tool, yes, for us content designers, but could we adopt
this for individual users so they could tweak [the model]?” However, they
acknowledged that granting end-users complete control over adaptation may not
be ideal either, and designers are responsible for setting certain
constraints:

> “In my experience with human beings, you don’t want to give them a blue sky.
> You wanna give them things they can create on their own and feel like
> they’re in control. I could see us using [Canvil] to create something like 5
> or 6 different [model] personalities that users around the world could
> leverage.” [P10]

P11 also found the idea of balancing designerly and end-user adaptation
appealing, as they were concerned about model biases (see Section 6.3.1). They
proposed having “a preference or a setting or a dial based on your regional
settings where [LLM behavior] could be modulated.” With the rapid rise of LLM-
powered user experiences, we invite practitioners and researchers to more
deeply reason about who can be responsible for adaptation and when adaptation
should happen.

####  6.3.3. Areas of Additional Support

Designers’ also identified several areas for improvement to better support
designerly adaptation. For one, designers lacked a clear mental model of how
the fields of Canvil would impact the adaptation outcome, including how much
detail is required in authoring the prompt. As a result, some designers wrote
long, detailed instructions, while others kept their Canvils sparse, and some
were pleasantly surprised by how well the model handled minimal instructions.
P6 further noted that the stochastic nature of the model was particularly
challenging to work with when writing outputs to their designs: “every time I
generated the [response], I felt that they were different every time, so it
was not easy to predict what the next [response] would look like.” We note
that current LLMs are known to be challenging to be precisely controlled
through prompting, and there is a lack of transparency into (or even an
established understanding of) how prompting impacts LLM behaviors. However,
these are actively researched areas which can help improve the mental model of
Canvil’s users and its general effectiveness.

Additionally, some designers wanted to engage in finer-grained experimentation
and iteration by only focusing on a specific field at once. P7, who wanted to
iterate more on the Model Profile field, wondered if there was a way to
“decrease the size [of the other fields] and expand to view everything.” We
envision a modular future version of the tool where each field can be
separated, such that a user can mix-and-match different fields to form a
complete system prompt. However, before that, we may need to address the
precise mapping between each field (and potential overlaps between them, as
noted by some participants) to adaptation outcomes as discussed above. As
mentioned in Section 4.3, our fields present just one possible structure for
system prompts. The fields can perhaps be reconfigured to reduce potential
overlap, or even dynamically generated based on the design task.

On a higher level, it would be irresponsible to assume that designers can walk
away with a comprehensive understanding of model behavior after a few rounds
of tinkering in Canvil. While observing a few informative output instances of
model behavior aids the design process, formal evaluations ensuring
comprehensive coverage of the user input space are crucial for production-
ready systems. Thus, new evaluation tooling and processes that loop in
technical stakeholders may be required.

##  7\. Discussion

Our findings shed light on possible workflows for designerly adaptation,
promises of tools for collaborative AI tinkering, and implications of
materiality on the social and collaborative practices of product teams. We
discuss each below.

###  7.1. A Workflow for Designerly Adaptation

Drawing on the results from both our formative and design study, we now
propose a workflow for designerly adaptation. Our proposed workflow consists
of four steps:

  1. (1)

Understand deployment context through user research. To orient adaptation, it
is imperative to first understand the context in which users will interact
with the LLM-powered system. This includes users’ goals, needs, and pain
points, along with customs and values that may affect their use of the
technology. Those with expertise in user research methodologies should lead
the execution of this step.

  2. (2)

Embed user research insights into system prompts, learning from examples where
possible. This carves out a direct path for user research to impact model
behavior, as we observed in Section 6.1.1. Thanks to the collaborative nature
of many modern design tools (Feng et al., 2023c), there may be example prompts
written by other designers available for reference, or templates to use as a
starting point (Section 6.2.1). By leveraging collaborative affordances to
share knowledge, designers can enhance their efficiency at adaptation over
time.

  3. (3)

Co-evolve interface design and prompts. As observed in Section 6.1.2,
adaptation can supply new inspiration for UI designs and affordances. On the
other hand, designers also tinkered with prompts to coax the model into
providing outputs that fit into the constraints laid out by the designs being
explored. We see the co-evolution of designs and prompts as a promising path
forward, in which iterative tinkering with system prompts for adaptation
shapes design choices, and vice versa.

  4. (4)

Share designs and adaptation efforts with the broader team. Showcasing in-
progress work through design critiques is already a part of the design process
(Feng et al., 2023b). In our study, we found that sharing Canvils helped
envision new collaborative workflows with other designers, as well as
communicating their perspectives and negotiating with technical stakeholders
(Section 6.2.1). Following from Step 3, we thus believe that sharing designs
alongside adaptation is critical for involving designers and leveraging their
expertise for responsible development and deployment of LLM-powered systems.

Our proposal is not meant to constrain what workflows for designerly
adaptation can possibly look like, but rather to offer a concrete entry point
for practitioners and researchers to further explore and iterate on this new
practice. We invite the community to experiment with this workflow in future
practice and research.

###  7.2. Towards Collaborative Tinkering With AI

Our work highlights the promises of not only empowering designers to tinker
with LLMs as a design material, but also collaboratively doing so. During our
design study, designers proposed various collaborative workflows for system
prompting with Canvil, including a divide-and-conquer approach to gather
expertise distributed across the team and a tinker-and-unite approach where
each designer tinkers independently before merging the results into a team
version (Section 6.2.1). Additionally, designers benefited from seeing and
learning from others’ Canvils and commented on more organized knowledge
sharing and version management afforded by using Canvil in Figma’s multiplayer
canvas environment (Section 6.2.2).

Despite promising opportunities, many tools that lower the barrier for AI
tinkering (e.g., (Lobe.ai, 2021; Liner.ai, 2022; Carney et al., 2020;
Subramonyam et al., 2021a)) were designed with individualized workflows in
mind. Some, like Google’s Teachable Machine (Carney et al., 2020), offer
social features such as project galleries to showcase example projects, but
affordances for collaboration remain limited. One reason may be technical
constraints surrounding the collaborative updating of computationally heavy
stages of large-scale training pipelines. The shift towards using large pre-
trained models, and the ability to interact with these models via natural
language, helps accelerate model adaptation and tightens feedback loops during
tinkering. Consequently, novel interfaces, such as node-based editors, (e.g.,
(Arawjo et al., 2023; Suh et al., 2023; Wu et al., 2022b; Angert et al.,
2023)), enable users to decompose large tasks by “chaining” together smaller
prompt submodules. Node-based editors are conceptually appealing for
collaboration (Wu et al., 2022b)—collaborators may distribute work according
to expertise and nature of the task within the submodule, and easily chain
together their work. Few node-based editors, however, support multiplayer
collaboration. We see significant potential in extending these tools along a
collaborative dimension.

Besides technical constraints discussed, it can simply be the case that robust
collaborative experiences are generally challenging to implement. We
circumvented this in our work by building on top of the Figma API, which
allows us to leverage Figma’s built-in collaborative features by default. We
thus encourage researchers and practitioners who wish to build collaborative
tools to take advantage of existing collaborative platforms’ developer APIs
where possible, especially as these APIs become more richly featured.

###  7.3. Materiality and Sociomateriality of LLMs

Throughout this work, we depict LLMs as a design material to use materiality
as a means of understanding, shaping, and applying this technology to tackle
design problems. Yet, in collaborative settings, materiality’s implications
extend beyond the individual. Scholars use the term sociomateriality in
recognition of materiality’s tendency to shape, and be shaped by,
organizational practices typically constituted as “social” (e.g., decision-
making, strategy formulation) (Orlikowski, 2007; Leonardi, 2012). For example,
Orlikowski observed that the issuance of BlackBerry devices within a company
led employees to obsessively check for new messages and send immediate
responses (Orlikowski, 2007). The BlackBerrys’ material properties—in this
case, being able to receive and send messages on-the-go—reconfigured
employees’ social practices, which in turn shifted how they think and act with
the technology.

In our design study, designerly adaptation allowed for exploration of LLMs’
material properties through tinkering. As a result, designers proposed various
UI affordances for enhancing users’ interaction with the LLM-powered meal
planner (Section 6.1.2) and adjusted the model’s guardrails depending on
differing dietary restrictions between user groups. However, grappling with
the materiality of LLMs also demonstrated potential to reconfigure social
practices within product teams. For example, designers informed us that they
saw new avenues of collaboration upon interaction with Canvil, which included
“handing off” system prompts to data scientists by exporting prompts out of
Figma, working with PMs to inform prompts with product requirements, and
having a more concrete artifact to communicate desired tweaks to engineers
(Section 6.2.3). Moreover, designerly adaptation itself may only be one piece
of the broader adaptation puzzle. Certain capabilities such as storing
external knowledge in model weights through fine-tuning cannot easily be
unlocked through—and may even a prerequisite for—designerly adaptation, paving
the way for processes and artifacts that fundamentally differ from those used
in tradition design handoffs (Leiva et al., 2019; Feng and Zhang, 2022). This
potential reconfiguration of teams’ collaborative dynamics establishes
designerly adaptation as not only a means of exploring LLMs’ materiality, but
also a sociomaterial practice.

The implications of this observation are twofold. First, sociomateriality
argues that reconfigurations of collaborative practices upon interaction with
a prominent new technology are inevitable (Orlikowski, 2007; Leonardi, 2012).
At the time of writing, frenzied excitement over LLM capabilities has launched
an industry-wide race to embed them into products and product suites (Parnin
et al., 2023). There is much yet to be discovered about shifts in
organizational practices and the emergence of new ones in the midst of this
race, so researchers in CSCW and organizational science should be attuned to
emergent challenges. Second, addressing these challenges for practitioners and
providing probes for researchers to study them may require new processes and
tools. Canvil is an early example of such a tool, but more are needed to
tackle the multiplicity of open questions in today’s rapidly evolving AI
landscape.

##  8\. Limitations and Future Work

Our design study, conducted in Figma, aimed to mirror real-world design
activities (Ball, 2005), yet concerns about ecological validity may persist.
User research in practice may differ in presentation and detail than in our
study setup, which can impact how designers perform research-informed
adaptation. Feasto, the fictitious app in our design study, applied LLMs to
the universally relatable topic of food. Designers in domains with fewer
broadly-shared experiences (e.g., accessibility), may require deeper
collaboration with domain experts and thus face workflow complexities not
accounted for in our study. A potential direction for future work, then, is
longitudinal studies that use contextual inquiry to observe product teams
throughout a full development lifecycle of an LLM-powered feature, focusing on
key adaptation decision-making processes and the practitioners involved.

Our study, like any study that uses a probe, has results contingent on our
probe’s features. For example, designers’ interaction with the Main
Form—designed with recommended practices for system prompting (Microsoft,
2023; OpenAI, 2023c)—shaped their approach to adaptation, and changes to the
form could impact existing results and reveal new ones. We mitigate this by
presenting findings not tied to the Main Form, nor our design study task.
Future research can build probes for designerly adaptation, distinct from
Canvil, to explore agreements with and divergences from our results.

Designers in our study also provided feedback on Canvil that we can integrate
into future work. These include breaking down the Main Form into sub-Canvils
and linking them together to assemble system prompts, offering support for
multimodal models (e.g., GPT-4V121212At the time of our study, GPT-4V was not
yet widely accessible via APIs, so the most advanced model available for use
in Canvil was GPT-4.), and text formatting controls for model outputs.
Finally, as mentioned in Section 4.4, Canvil may also be used in FigJam.
Future research could explore adaptation in early design stages, like
brainstorming and ideation, through studies in FigJam.

##  9\. Conclusion

In this paper, we introduced the practice of designerly adaptation to
encourage designers to engage with LLMs as an adaptable design material when
crafting LLM-powered user experiences. We first built up a characterization of
designerly adaptation through a formative interview study with 12 designers.
We then developed Canvil, a Figma widget that operationalizes designerly
adaptation by enabling designers to iteratively author, test, and share system
prompts within Figma’s collaborative design environment. We used Canvil as a
technology probe to explore the integration of designerly adaptation in UX
practice through a group-based design study with 17 designers. We observed
numerous promising approaches taken by designers such as iteratively tinkering
with different adaptation strategies, sensitizing the model to nuances in user
needs, and reasoning about interface affordances using research-informed model
behavior; these approaches’ promises were amplified once adaptation was
embraced as a collaborative practice. Our work illuminates paths for new
processes and tools to spotlight designers’ user-centered perspectives and
expertise for more responsible and thoughtful deployment of LLM-powered
technologies.

## References

  * (1)
  * Amershi et al. (2015) Saleema Amershi, Max Chickering, Steven M Drucker, Bongshin Lee, Patrice Simard, and Jina Suh. 2015.  Modeltracker: Redesigning performance analysis tools for machine learning. In _Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems_. 337–346.
  * Angert et al. (2023) Tyler Angert, Miroslav Suzara, Jenny Han, Christopher Pondoc, and Hariharan Subramonyam. 2023.  Spellburst: A Node-Based Interface for Exploratory Creative Coding with Natural Language Prompts. In _Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology_ (¡conf-loc¿, ¡city¿San Francisco¡/city¿, ¡state¿CA¡/state¿, ¡country¿USA¡/country¿, ¡/conf-loc¿) _(UIST ’23)_. Association for Computing Machinery, New York, NY, USA, Article 100, 22 pages.  https://doi.org/10.1145/3586183.3606719
  * Apple (2024) Apple. 2024.  Machine Learning—Human Interface Guidelines.  https://developer.apple.com/design/human-interface-guidelines/technologies/machine-learning/introduction.
  * Arawjo et al. (2023) Ian Arawjo, Priyan Vaithilingam, Chelse Swoopes, Martin Wattenberg, and Elena Glassman. 2023.  ChainForge.  https://www.chainforge.ai/.  Accessed: 2023-07-21.
  * Bai et al. (2022) Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. 2022\.  Constitutional ai: Harmlessness from ai feedback.  _arXiv preprint arXiv:2212.08073_ (2022).
  * Ball (2005) Jonathan Ball. 2005.  The Double Diamond: A universally accepted depiction of the design process.  https://www.designcouncil.org.uk/news-opinion/double-diamond-universally-accepted-depiction-design-process.
  * Bäuerle et al. (2022) Alex Bäuerle, Ángel Alexander Cabrera, Fred Hohman, Megan Maher, David Koski, Xavier Suau, Titus Barik, and Dominik Moritz. 2022.  Symphony: Composing Interactive Interfaces for Machine Learning. In _Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems_ (New Orleans, LA, USA) _(CHI ’22)_. Association for Computing Machinery, New York, NY, USA, Article 210, 14 pages.  https://doi.org/10.1145/3491102.3502102
  * Benjamin et al. (2021) Jesse Josua Benjamin, Arne Berger, Nick Merrill, and James Pierce. 2021.  Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry. In _Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_ (Yokohama, Japan) _(CHI ’21)_. Association for Computing Machinery, New York, NY, USA, Article 171, 14 pages.  https://doi.org/10.1145/3411764.3445481
  * Beyer and Holtzblatt (1999) Hugh Beyer and Karen Holtzblatt. 1999.  Contextual design.  _interactions_ 6, 1 (1999), 32–42.
  * Bodén et al. (2021) Anna CS Bodén, Jesper Molin, Stina Garvin, Rebecca A West, Claes Lundström, and Darren Treanor. 2021.  The human-in-the-loop: an evaluation of pathologists’ interaction with artificial intelligence in clinical practice.  _Histopathology_ 79, 2 (2021), 210–218.
  * Branson et al. (2010) Steve Branson, Catherine Wah, Florian Schroff, Boris Babenko, Peter Welinder, Pietro Perona, and Serge Belongie. 2010.  Visual recognition with humans in the loop. In _Computer Vision–ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part IV 11_. Springer, 438–451.
  * Brooke (1995) John Brooke. 1995.  SUS: A quick and dirty usability scale.  _Usability Eval. Ind._ 189 (11 1995).
  * Brown et al. (2020) Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020\.  Language models are few-shot learners.  _Advances in neural information processing systems_ 33 (2020), 1877–1901.
  * Cabrera et al. (2023) Ángel Alexander Cabrera, Erica Fu, Donald Bertucci, Kenneth Holstein, Ameet Talwalkar, Jason I Hong, and Adam Perer. 2023.  Zeno: An interactive framework for behavioral evaluation of machine learning. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_. 1–14.
  * Carney et al. (2020) Michelle Carney, Barron Webster, Irene Alvarado, Kyle Phillips, Noura Howell, Jordan Griffith, Jonas Jongejan, Amit Pitaru, and Alexander Chen. 2020.  Teachable Machine: Approachable Web-Based Tool for Exploring Machine Learning Classification. In _Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems_ (Honolulu, HI, USA) _(CHI EA ’20)_. Association for Computing Machinery, New York, NY, USA, 1–8.  https://doi.org/10.1145/3334480.3382839
  * Chao et al. (2010) Crystal Chao, Maya Cakmak, and Andrea L Thomaz. 2010.  Transparent active learning for robots. In _2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI)_. IEEE, 317–324.
  * Chen et al. (2022) Quan Ze Chen, Tobias Schnabel, Besmira Nushi, and Saleema Amershi. 2022.  HINT: Integration Testing for AI-Based Features with Humans in the Loop. In _27th International Conference on Intelligent User Interfaces_ (Helsinki, Finland) _(IUI ’22)_. Association for Computing Machinery, New York, NY, USA, 549–565.  https://doi.org/10.1145/3490099.3511141
  * Cheng et al. (2023) Myra Cheng, Esin Durmus, and Dan Jurafsky. 2023.  Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models. In _Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_. Association for Computational Linguistics, Toronto, Canada, 1504–1532.  https://doi.org/10.18653/v1/2023.acl-long.84
  * Christiano et al. (2017) Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017.  Deep reinforcement learning from human preferences.  _Advances in neural information processing systems_ 30 (2017).
  * Deng et al. (2023) Wesley Hanwen Deng, Nur Yildirim, Monica Chang, Motahhare Eslami, Kenneth Holstein, and Michael Madaio. 2023.  Investigating Practices and Opportunities for Cross-Functional Collaboration around AI Fairness in Industry Practice. In _Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency_ (Chicago, IL, USA) _(FAccT ’23)_. Association for Computing Machinery, New York, NY, USA, 705–716.  https://doi.org/10.1145/3593013.3594037
  * Deshpande et al. (2023) Ameet Deshpande, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, and Karthik Narasimhan. 2023.  Toxicity in chatgpt: Analyzing persona-assigned language models.  _arXiv preprint arXiv:2304.05335_ (2023).
  * Do et al. (2023) Kimberly Do, Rock Yuren Pang, Jiachen Jiang, and Katharina Reinecke. 2023.  “That’s Important, but…”: How Computer Science Researchers Anticipate Unintended Consequences of Their Research Innovations. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_ (, Hamburg, Germany,) _(CHI ’23)_. Association for Computing Machinery, New York, NY, USA, Article 602, 16 pages.  https://doi.org/10.1145/3544548.3581347
  * Dodge et al. (2020) Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, and Noah Smith. 2020.  Fine-tuning pretrained language models: Weight initializations, data orders, and early stopping.  _arXiv preprint arXiv:2002.06305_ (2020).
  * Dove et al. (2017) Graham Dove, Kim Halskov, Jodi Forlizzi, and John Zimmerman. 2017.  UX Design Innovation: Challenges for Working with Machine Learning as a Design Material. In _Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems_ (Denver, Colorado, USA) _(CHI ’17)_. Association for Computing Machinery, New York, NY, USA, 278–288.  https://doi.org/10.1145/3025453.3025739
  * Du et al. (2023) Ruofei Du, Na Li, Jing Jin, Michelle Carney, Scott Miles, Maria Kleiner, Xiuxiu Yuan, Yinda Zhang, Anuva Kulkarni, Xingyu Liu, Ahmed Sabie, Sergio Orts-Escolano, Abhishek Kar, Ping Yu, Ram Iyengar, Adarsh Kowdle, and Alex Olwal. 2023.  Rapsai: Accelerating Machine Learning Prototyping of Multimedia Applications through Visual Programming. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_ (Hamburg, Germany) _(CHI ’23)_. Association for Computing Machinery, New York, NY, USA, Article 125, 23 pages.  https://doi.org/10.1145/3544548.3581338
  * Fails and Olsen (2003) Jerry Alan Fails and Dan R. Olsen. 2003.  Interactive Machine Learning. In _Proceedings of the 8th International Conference on Intelligent User Interfaces_ (Miami, Florida, USA) _(IUI ’03)_. Association for Computing Machinery, New York, NY, USA, 39–45.  https://doi.org/10.1145/604045.604056
  * Feng et al. (2023a) KJ Feng, Quan Ze Chen, Inyoung Cheong, King Xia, Amy X Zhang, et al. 2023a.  Case Repositories: Towards Case-Based Reasoning for AI Alignment.  _arXiv preprint arXiv:2311.10934_ (2023).
  * Feng et al. (2023b) KJ Kevin Feng, Maxwell James Coppock, and David W McDonald. 2023b.  How Do UX Practitioners Communicate AI as a Design Material? Artifacts, Conceptions, and Propositions. In _Proceedings of the 2023 ACM Designing Interactive Systems Conference_. 2263–2280.
  * Feng et al. (2023c) KJ Kevin Feng, Tony W Li, and Amy X Zhang. 2023c.  Understanding Collaborative Practices and Tools of Professional UX Practitioners in Software Organizations. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_. 1–20.
  * Feng and McDonald (2023) KJ Kevin Feng and David W McDonald. 2023.  Addressing UX Practitioners’ Challenges in Designing ML Applications: an Interactive Machine Learning Approach. In _Proceedings of the 28th International Conference on Intelligent User Interfaces_. 337–352.
  * Feng and Zhang (2022) KJ Kevin Feng and Amy X. Zhang. 2022.  From Handofs to Co-Creation: Deepening Collaboration between Designers, Developers, and Data Science Workers in UX Design. In _Proceedings of the InContext: Futuring User-Experience Design Tools Workshop at CHI Conference on Human Factors in Computing Systems (CHI ’22)_ (New Orleans, LA, USA).  https://hcibook.net/incontext/wp-content/uploads/sites/5/2022/04/FromHandofs-to-Co-Creation-Deepening-Collaboration-between-DesignersDevelopers-and-Data-Science-Workers-in-UX-Design-1.pdf
  * Google (2024) Google. 2024.  Cloud AutoML Custom Machine Learning Models.  https://cloud.google.com/automl/.
  * Google (2024) Google. 2024.  People + AI Guidebook.  https://pair.withgoogle.com/guidebook/.
  * Görtler et al. (2022) Jochen Görtler, Fred Hohman, Dominik Moritz, Kanit Wongsuphasawat, Donghao Ren, Rahul Nair, Marc Kirchner, and Kayur Patel. 2022.  Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels. In _Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems_ (New Orleans, LA, USA) _(CHI ’22)_. Association for Computing Machinery, New York, NY, USA, Article 408, 13 pages.  https://doi.org/10.1145/3491102.3501823
  * Hohman et al. (2019) Fred Hohman, Andrew Head, Rich Caruana, Robert DeLine, and Steven M. Drucker. 2019.  Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models. In _Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems_ (Glasgow, Scotland Uk) _(CHI ’19)_. Association for Computing Machinery, New York, NY, USA, 1–13.  https://doi.org/10.1145/3290605.3300809
  * Hohman et al. (2020) Fred Hohman, Kanit Wongsuphasawat, Mary Beth Kery, and Kayur Patel. 2020.  Understanding and Visualizing Data Iteration in Machine Learning. In _Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems_ (Honolulu, HI, USA) _(CHI ’20)_. Association for Computing Machinery, New York, NY, USA, 1–13.  https://doi.org/10.1145/3313831.3376177
  * Hu et al. (2021) Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021.  Lora: Low-rank adaptation of large language models.  _arXiv preprint arXiv:2106.09685_ (2021).
  * Huffman and Woodward (2023) Scott Huffman and Josh Woodward. 2023.  PaLM API & MakerSuite: an approachable way to start prototyping and building generative AI applications.  https://developers.googleblog.com/2023/03/announcing-palm-api-and-makersuite.html.
  * Hutchinson et al. (2003) Hilary Hutchinson, Wendy Mackay, Bo Westerlund, Benjamin B Bederson, Allison Druin, Catherine Plaisant, Michel Beaudouin-Lafon, Stéphane Conversy, Helen Evans, Heiko Hansen, et al. 2003\.  Technology probes: inspiring design for and with families. In _Proceedings of the SIGCHI conference on Human factors in computing systems_. 17–24.
  * IBM (2024) IBM. 2024.  IBM Watson Studio - AutoML - IBM AutoAI.  https://www.ibm.com/cloud/watson-studio/autoai.
  * Jiang et al. (2022) Ellen Jiang, Kristen Olson, Edwin Toh, Alejandra Molina, Aaron Donsbach, Michael Terry, and Carrie J Cai. 2022.  PromptMaker: Prompt-Based Prototyping with Large Language Models. In _Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems_ (New Orleans, LA, USA) _(CHI EA ’22)_. Association for Computing Machinery, New York, NY, USA, Article 35, 8 pages.  https://doi.org/10.1145/3491101.3503564
  * Jörke et al. (2023) Matthew Jörke, Yasaman S. Sefidgar, Talie Massachi, Jina Suh, and Gonzalo Ramos. 2023.  Pearl: A Technology Probe for Machine-Assisted Reflection on Personal Data. In _Proceedings of the 28th International Conference on Intelligent User Interfaces_ (Sydney, NSW, Australia) _(IUI ’23)_. Association for Computing Machinery, New York, NY, USA, 902–918.  https://doi.org/10.1145/3581641.3584054
  * Kaddour et al. (2023) Jean Kaddour, Joshua Harris, Maximilian Mozes, Herbie Bradley, Roberta Raileanu, and Robert McHardy. 2023.  Challenges and Applications of Large Language Models.  _arXiv preprint arXiv:2307.10169_ (2023).
  * Kandel et al. (2011) Sean Kandel, Andreas Paepcke, Joseph Hellerstein, and Jeffrey Heer. 2011.  Wrangler: Interactive visual specification of data transformation scripts. In _Proceedings of the sigchi conference on human factors in computing systems_. 3363–3372.
  * Kulkarni et al. (2023) Chinmay Kulkarni, Stefania Druga, Minsuk Chang, Alex Fiannaca, Carrie Cai, and Michael Terry. 2023.  A Word is Worth a Thousand Pictures: Prompts as AI Design Material.  _arXiv preprint arXiv:2303.12647_ (2023).
  * Lage et al. (2018) Isaac Lage, Andrew Ross, Samuel J Gershman, Been Kim, and Finale Doshi-Velez. 2018.  Human-in-the-loop interpretability prior.  _Advances in neural information processing systems_ 31 (2018).
  * Lee et al. (2022) Mina Lee, Percy Liang, and Qian Yang. 2022.  Coauthor: Designing a human-ai collaborative writing dataset for exploring language model capabilities. In _Proceedings of the 2022 CHI conference on human factors in computing systems_. 1–19.
  * Leiva et al. (2019) Germán Leiva, Nolwenn Maudet, Wendy Mackay, and Michel Beaudouin-Lafon. 2019.  Enact: Reducing Designer–Developer Breakdowns When Prototyping Custom Interactions.  26, 3, Article 19 (may 2019), 48 pages.  https://doi.org/10.1145/3310276
  * Leonardi (2012) Paul M Leonardi. 2012.  Materiality, sociomateriality, and socio-technical systems: What do these terms mean? How are they different? Do we need them?  _Materiality and organizing: Social interaction in a technological world_ 25, 10 (2012), 10–1093.
  * Liao et al. (2023) Q. Vera Liao, Hariharan Subramonyam, Jennifer Wang, and Jennifer Wortman Vaughan. 2023.  Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_ (Hamburg, Germany) _(CHI ’23)_. Association for Computing Machinery, New York, NY, USA, Article 9, 21 pages.  https://doi.org/10.1145/3544548.3580652
  * Liao and Vaughan (2023) Q Vera Liao and Jennifer Wortman Vaughan. 2023.  AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap.  _arXiv preprint arXiv:2306.01941_ (2023).
  * Liner.ai (2022) Liner.ai. 2022.  Machine learning in a few clicks.  https://www.liner.ai/.
  * Liu et al. (2020) Yang Liu, Alex Kale, Tim Althoff, and Jeffrey Heer. 2020.  Boba: Authoring and visualizing multiverse analyses.  _IEEE Transactions on Visualization and Computer Graphics_ 27, 2 (2020), 1753–1763.
  * Lobe.ai (2021) Lobe.ai. 2021.  Machine Learning Made Easy.  https://www.lobe.ai/.
  * Luciani et al. (2018) Danwei Tran Luciani, Martin Lindvall, Jonas Löwgren, et al. 2018\.  Machine learning as a design material: a curated collection of exemplars for visual interaction.  _DS 91: Proceedings of NordDesign 2018, Linköping, Sweden, 14th-17th August 2018_ (2018).
  * Lury and Wakeford (2012) Celia Lury and Nina Wakeford. 2012.  _Inventive methods: The happening of the social_.  Routledge.
  * Microsoft (2023) Microsoft. 2023.  System message framework and template recommendations for Large Language Models (LLMs).  https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/system-message.
  * Microsoft (2024) Microsoft. 2024.  Azure Automated Machine Learning - AutoML.  https://azure.microsoft.com/en-us/products/machine-learning/automatedml/.
  * Microsoft (2024) Microsoft. 2024.  Collaborative tools to help you create more effective and responsible human-AI experiences.  https://www.microsoft.com/en-us/haxtoolkit/.
  * Mishra and Rzeszotarski (2021) Swati Mishra and Jeffrey M Rzeszotarski. 2021.  Designing Interactive Transfer Learning Tools for ML Non-Experts. In _Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_ (Yokohama, Japan) _(CHI ’21)_. Association for Computing Machinery, New York, NY, USA, Article 364, 15 pages.  https://doi.org/10.1145/3411764.3445096
  * Monarch (2021) Robert Munro Monarch. 2021.  _Human-in-the-Loop Machine Learning: Active learning and annotation for human-centered AI_.  Simon and Schuster.
  * Moore et al. (2023) Steven Moore, Q Vera Liao, and Hariharan Subramonyam. 2023.  fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Tasks. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_. 1–19.
  * Nguyen and Choo (2021) Tien N Nguyen and Raymond Choo. 2021.  Human-in-the-loop xai-enabled vulnerability detection, investigation, and mitigation. In _2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE)_. IEEE, 1210–1212.
  * OpenAI (2023a) OpenAI. 2023a.  Custom instructions for ChatGPT.  https://openai.com/blog/custom-instructions-for-chatgpt.
  * OpenAI (2023b) OpenAI. 2023b.  GPT-4 Technical Report.  _ArXiv_ abs/2303.08774 (2023).
  * OpenAI (2023c) OpenAI. 2023c.  GPT Models.  https://platform.openai.com/docs/guides/gpt.
  * OpenAI (2024) OpenAI. 2024.  Playground.  https://platform.openai.com/playground.
  * Orlikowski (2007) Wanda J Orlikowski. 2007.  Sociomaterial practices: Exploring technology at work.  _Organization studies_ 28, 9 (2007), 1435–1448.
  * Ouyang et al. (2022a) Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022a.  Training language models to follow instructions with human feedback.  _Advances in Neural Information Processing Systems_ 35 (2022), 27730–27744.
  * Ouyang et al. (2022b) Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike, and Ryan Lowe. 2022b.  Training language models to follow instructions with human feedback. In _Advances in Neural Information Processing Systems_ , S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35. Curran Associates, Inc., 27730–27744.  https://proceedings.neurips.cc/paper_files/paper/2022/file/b1efde53be364a73914f58805a001731-Paper-Conference.pdf
  * Parnin et al. (2023) Chris Parnin, Gustavo Soares, Rahul Pandita, Sumit Gulwani, Jessica Rich, and Austin Z Henley. 2023.  Building Your Own Product Copilot: Challenges, Opportunities, and Needs.  _arXiv preprint arXiv:2312.14231_ (2023).
  * Petridis et al. (2023a) Savvas Petridis, Michael Terry, and Carrie Jun Cai. 2023a.  PromptInfuser: Bringing User Interface Mock-Ups to Life with Large Language Models. In _Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems_ (Hamburg, Germany) _(CHI EA ’23)_. Association for Computing Machinery, New York, NY, USA, Article 237, 6 pages.  https://doi.org/10.1145/3544549.3585628
  * Petridis et al. (2023b) Savvas Petridis, Michael Terry, and Carrie J Cai. 2023b.  PromptInfuser: How Tightly Coupling AI and UI Design Impacts Designers’ Workflows.  _arXiv preprint arXiv:2310.15435_ (2023).
  * Pichai (2023) Sundar Pichai. 2023.  An important next step on our AI journey.  https://blog.google/technology/ai/bard-google-ai-search-updates/.
  * Qian et al. (2019) Kun Qian, Lucian Popa, and Prithviraj Sen. 2019.  Systemer: A human-in-the-loop system for explainable entity resolution.  _Proceedings of the VLDB Endowment_ 12, 12 (2019), 1794–1797.
  * Rafailov et al. (2023) Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D Manning, and Chelsea Finn. 2023.  Direct preference optimization: Your language model is secretly a reward model.  _arXiv preprint arXiv:2305.18290_ (2023).
  * Raghavan et al. (2006) Hema Raghavan, Omid Madani, and Rosie Jones. 2006.  Active learning with feedback on features and instances.  _The Journal of Machine Learning Research_ 7 (2006), 1655–1686.
  * Ren et al. (2016) Donghao Ren, Saleema Amershi, Bongshin Lee, Jina Suh, and Jason D Williams. 2016.  Squares: Supporting interactive performance analysis for multiclass classifiers.  _IEEE transactions on visualization and computer graphics_ 23, 1 (2016), 61–70.
  * Robertson et al. (2023) Samantha Robertson, Zijie J Wang, Dominik Moritz, Mary Beth Kery, and Fred Hohman. 2023.  Angler: Helping Machine Translation Practitioners Prioritize Model Improvements. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_. 1–20.
  * Robles and Wiberg (2010) Erica Robles and Mikael Wiberg. 2010.  Texturing the ”Material Turn” in Interaction Design. In _Proceedings of the Fourth International Conference on Tangible, Embedded, and Embodied Interaction_ (Cambridge, Massachusetts, USA) _(TEI ’10)_. Association for Computing Machinery, New York, NY, USA, 137–144.  https://doi.org/10.1145/1709886.1709911
  * Roose (2023) Kevin Roose. 2023.  Bing’s A.I. Chat: ‘I Want to Be Alive.’.  https://www.nytimes.com/2023/02/16/technology/bing-chatbot-transcript.html.
  * Salewski et al. (2023) Leonard Salewski, Stephan Alaniz, Isabel Rio-Torto, Eric Schulz, and Zeynep Akata. 2023.  In-Context Impersonation Reveals Large Language Models’ Strengths and Biases.  _arXiv preprint arXiv:2305.14930_ (2023).
  * Santurkar et al. (2023) Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto. 2023.  Whose opinions do language models reflect?  _arXiv preprint arXiv:2303.17548_ (2023).
  * Selbst et al. (2019) Andrew D. Selbst, Danah Boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, and Janet Vertesi. 2019.  Fairness and Abstraction in Sociotechnical Systems. In _Proceedings of the Conference on Fairness, Accountability, and Transparency_ (Atlanta, GA, USA) _(FAT* ’19)_. Association for Computing Machinery, New York, NY, USA, 59–68.  https://doi.org/10.1145/3287560.3287598
  * Settles (2012) Burr Settles. 2012.  Active learning.  _Synthesis lectures on artificial intelligence and machine learning_ 6, 1 (2012), 1–114.
  * Shah (2023) Saqib Shah. 2023.  Snapchat’s My AI chatbot is making people paranoid as it ‘knows your current location’.  https://www.standard.co.uk/tech/snapchat-my-ai-chatbot-making-people-paranoid-b1076287.html.
  * Smith et al. (2018) Alison Smith, Varun Kumar, Jordan Boyd-Graber, Kevin Seppi, and Leah Findlater. 2018.  Closing the Loop: User-Centered Design and Evaluation of a Human-in-the-Loop Topic Modeling System. In _23rd International Conference on Intelligent User Interfaces_ (Tokyo, Japan) _(IUI ’18)_. Association for Computing Machinery, New York, NY, USA, 293–304.  https://doi.org/10.1145/3172944.3172965
  * Stallbaumer (2023) Colette Stallbaumer. 2023.  Introducing Microsoft 365 Copilot—A whole new way to work.  https://www.microsoft.com/en-us/microsoft-365/blog/2023/03/16/introducing-microsoft-365-copilot-a-whole-new-way-to-work/.
  * Star and Griesemer (1989) Susan Leigh Star and James R Griesemer. 1989.  Institutional ecology,translations’ and boundary objects: Amateurs and professionals in Berkeley’s Museum of Vertebrate Zoology, 1907-39.  _Social studies of science_ 19, 3 (1989), 387–420.
  * Subramonyam et al. (2022) Hariharan Subramonyam, Jane Im, Colleen Seifert, and Eytan Adar. 2022.  Solving Separation-of-Concerns Problems in Collaborative Design of Human-AI Systems through Leaky Abstractions. In _Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems_ (New Orleans, LA, USA) _(CHI ’22)_. Association for Computing Machinery, New York, NY, USA, Article 481, 21 pages.  https://doi.org/10.1145/3491102.3517537
  * Subramonyam et al. (2021a) Hariharan Subramonyam, Colleen Seifert, and Eytan Adar. 2021a.  ProtoAI: Model-Informed Prototyping for AI-Powered Interfaces. In _26th International Conference on Intelligent User Interfaces_ (College Station, TX, USA) _(IUI ’21)_. Association for Computing Machinery, New York, NY, USA, 48–58.  https://doi.org/10.1145/3397481.3450640
  * Subramonyam et al. (2021b) Hariharan Subramonyam, Colleen Seifert, and Eytan Adar. 2021b.  Towards A Process Model for Co-Creating AI Experiences. In _Designing Interactive Systems Conference 2021_ (Virtual Event, USA) _(DIS ’21)_. Association for Computing Machinery, New York, NY, USA, 1529–1543.  https://doi.org/10.1145/3461778.3462012
  * Suh et al. (2023) Sangho Suh, Bryan Min, Srishti Palani, and Haijun Xia. 2023.  Sensecape: Enabling Multilevel Exploration and Sensemaking with Large Language Models. In _Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology_ (¡conf-loc¿, ¡city¿San Francisco¡/city¿, ¡state¿CA¡/state¿, ¡country¿USA¡/country¿, ¡/conf-loc¿) _(UIST ’23)_. Association for Computing Machinery, New York, NY, USA, Article 1, 18 pages.  https://doi.org/10.1145/3586183.3606756
  * usability.gov (2022) usability.gov. 2022.  System Usability Scale (SUS).  https://www.usability.gov/how-to-and-tools/methods/system-usability-scale.html.
  * Wang et al. (2023) Qiaosi Wang, Michael Madaio, Shaun Kane, Shivani Kapania, Michael Terry, and Lauren Wilcox. 2023.  Designing Responsible AI: Adaptations of UX Practice to Meet Responsible AI Challenges _(CHI ’23)_. Association for Computing Machinery, New York, NY, USA, Article 249, 16 pages.  https://doi.org/10.1145/3544548.3581278
  * Wang et al. (2022) Zijie J Wang, Chudi Zhong, Rui Xin, Takuya Takagi, Zhi Chen, Duen Horng Chau, Cynthia Rudin, and Margo Seltzer. 2022.  TimberTrek: Exploring and curating sparse decision trees with interactive visualization. In _2022 IEEE Visualization and Visual Analytics (VIS)_. IEEE, 60–64.
  * Wei et al. (2023) Alexander Wei, Nika Haghtalab, and Jacob Steinhardt. 2023.  Jailbroken: How Does LLM Safety Training Fail?  _arXiv preprint arXiv:2307.02483_ (2023).
  * Wei et al. (2021) Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021.  Finetuned language models are zero-shot learners.  _arXiv preprint arXiv:2109.01652_ (2021).
  * Wei et al. (2022) Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022\.  Chain-of-thought prompting elicits reasoning in large language models.  _Advances in Neural Information Processing Systems_ 35 (2022), 24824–24837.
  * Wolf et al. (2023) Yotam Wolf, Noam Wies, Yoav Levine, and Amnon Shashua. 2023.  Fundamental limitations of alignment in large language models.  _arXiv preprint arXiv:2304.11082_ (2023).
  * Wongsuphasawat et al. (2015) Kanit Wongsuphasawat, Dominik Moritz, Anushka Anand, Jock Mackinlay, Bill Howe, and Jeffrey Heer. 2015.  Voyager: Exploratory analysis via faceted browsing of visualization recommendations.  _IEEE transactions on visualization and computer graphics_ 22, 1 (2015), 649–658.
  * Wu et al. (2023) Sherry Wu, Hua Shen, Daniel S Weld, Jeffrey Heer, and Marco Tulio Ribeiro. 2023.  ScatterShot: Interactive In-context Example Curation for Text Transformation. In _Proceedings of the 28th International Conference on Intelligent User Interfaces_. 353–367.
  * Wu et al. (2022a) Tongshuang Wu, Ellen Jiang, Aaron Donsbach, Jeff Gray, Alejandra Molina, Michael Terry, and Carrie J Cai. 2022a.  Promptchainer: Chaining large language model prompts through visual programming. In _CHI Conference on Human Factors in Computing Systems Extended Abstracts_. 1–10.
  * Wu et al. (2022b) Tongshuang Wu, Michael Terry, and Carrie Jun Cai. 2022b.  Ai chains: Transparent and controllable human-ai interaction by chaining large language model prompts. In _Proceedings of the 2022 CHI conference on human factors in computing systems_. 1–22.
  * Yang (2018) Qian Yang. 2018.  Machine learning as a UX design material: how can we imagine beyond automation, recommenders, and reminders?. In _AAAI Spring Symposia_.
  * Yang et al. (2020) Qian Yang, Aaron Steinfeld, Carolyn Rosé, and John Zimmerman. 2020.  Re-Examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to Design. In _Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems_ (Honolulu, HI, USA) _(CHI ’20)_. Association for Computing Machinery, New York, NY, USA, 1–13.  https://doi.org/10.1145/3313831.3376301
  * Yildirim et al. (2022) Nur Yildirim, Alex Kass, Teresa Tung, Connor Upton, Donnacha Costello, Robert Giusti, Sinem Lacin, Sara Lovic, James M O’Neill, Rudi O’Reilly Meehan, et al. 2022\.  How Experienced Designers of Enterprise Applications Engage AI as a Design Material. In _Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems_. 1–13.
  * Yildirim et al. (2023) Nur Yildirim, Mahima Pushkarna, Nitesh Goyal, Martin Wattenberg, and Fernanda Viégas. 2023.  Investigating How Practitioners Use Human-AI Guidelines: A Case Study on the People + AI Guidebook. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_ (Hamburg, Germany) _(CHI ’23)_. Association for Computing Machinery, New York, NY, USA, Article 356, 13 pages.  https://doi.org/10.1145/3544548.3580900
  * Zamfirescu-Pereira et al. (2023) JD Zamfirescu-Pereira, Richmond Y Wong, Bjoern Hartmann, and Qian Yang. 2023.  Why Johnny can’t prompt: how non-AI experts try (and fail) to design LLM prompts. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_. 1–21.
  * Zhao et al. (2021) Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021.  Calibrate before use: Improving few-shot performance of language models. In _International Conference on Machine Learning_. PMLR, 12697–12706.

##  Appendix A Formative Study Participants

P# |  Job Title |  YoE |  LLM Application Area |  Education Background |  Region
---|---|---|---|---|---
P1 |  Product Designer |  6–10 |  Domain-specific Data QA |  Visual/Industrial Design; Computing |  Denmark
P2 |  Interaction Designer |  6–10 |  Conversational Search |  Visual/Industrial Design |  Canada
P3 |  Principal Product Designer |  6–10 |  Domain-specific Data QA |  Visual/Industrial Design |  U.S.
P4 |  Content Designer |  6–10 |  Conversational Search; Domain-specific Data QA |  Humanities |  U.S.
P5 |  Principal Content Designer |  6–10 |  Conversational Search |  Humanities |  U.S.
P6 |  Content Designer |  1–2 |  Recommendation |  Humanities |  U.S.
P7 |  UX Researcher |  3–5 |  Domain-specific Data QA |  Visual/Industrial Design; Social & Behavioral Sciences |  Ireland
P8 |  Product Designer |  3–5 |  Creativity Support Tools |  Visual/Industrial Design |  U.S.
P9 |  Senior Designer |  11+ |  Conversational Search; Creativity Support Tools |  Visual/Industrial Design |  U.S.
P10 |  UX Researcher |  11+ |  Domain-specific Data QA |  Social & Behavioral Sciences |  U.S.