# Canvil: Designerly Adaptation for LLM-Powered User Experiences

arXiv: https://arxiv.org/abs/2401.09051 · HTML: https://mlsanity.com/paper/2401.09051/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2024.

## Built on by (most cited first, 9 of 9)

- [Not Just Novelty: A Longitudinal Study on Utility and Customization of an AI Workflow](https://mlsanity.com/paper/2402.09894/) (2024)
- [Collaborative Document Editing with Multiple Users and AI Agents](https://mlsanity.com/paper/2509.11826/) (2025)
- [Disaggregated Prefill and Decoding Inference System for Large Language Model Serving on Multi-Vendor GPUs](https://mlsanity.com/paper/2509.17542/) (2025)
- [From Queries to Criteria: Understanding How Astronomers Evaluate LLMs](https://mlsanity.com/paper/2507.15715/) (2025)
- [Exploring the Innovation Opportunities for Pre-trained Models](https://mlsanity.com/paper/2505.15790/) (2025)
- [PolicyPad: Collaborative Prototyping of LLM Policies](https://mlsanity.com/paper/2509.19680/) (2025)
- [Personagram: Bridging Personas and Product Design for Creative Ideation with Multimodal LLMs](https://mlsanity.com/paper/2602.06197/) (2026)
- [Gricea: An Open Science Platform for Conversational AI Research](https://mlsanity.com/paper/2609.22039/) (2026)
- [Does AI Save Time on Product Design? A Randomized Controlled Experiment of AI Prompt-to-Design Workflows](https://mlsanity.com/paper/2609.26725/) (2026)

## Abstract (arXiv)

Advancements in large language models (LLMs) are sparking a proliferation of LLM-powered user experiences (UX). In product teams, designers often craft UX to meet user needs, but it is unclear how they engage with LLMs as a novel design material. Through a formative study with 12 designers, we find that designers seek a translational process that enables design requirements to shape and be shaped by LLM behavior, motivating a need for designerly adaptation to facilitate this translation. We then built Canvil, a Figma widget that operationalizes designerly adaptation. We used Canvil as a probe to study designerly adaptation in a group-based design study (6 groups, N=17), finding that designers constructively iterated on both adaptation approaches and interface designs to enhance end-user interaction with LLMs. Furthermore, designers identified promising collaborative workflows for designerly adaptation. Our work opens new avenues for processes and tools that foreground designers' human-centered expertise when developing LLM-powered applications.