Canvil: Designerly Adaptation for LLM-Powered User Experiences
CoRR(2024)
摘要
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 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 Canvil, 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.
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