# PromptInfuser: How Tightly Coupling AI and UI Design Impacts Designers' Workflows

arXiv: https://arxiv.org/abs/2310.15435 · HTML: https://mlsanity.com/paper/2310.15435/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 56 references that are in the map.
- First posted on arXiv 24 Oct 2023.

## Builds on (most cited first, 13 of 13)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Social Simulacra: Creating Populated Prototypes for Social Computing Systems](https://mlsanity.com/paper/2208.04024/) (2022)
- [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021)
- [Design Guidelines for Prompt Engineering Text-to-Image Generative Models](https://mlsanity.com/paper/2109.06977/) (2021)
- [PromptChainer: Chaining Large Language Model Prompts through Visual Programming](https://mlsanity.com/paper/2203.06566/) (2022)
- [Sparks: Inspiration for Science Writing using Language Models](https://mlsanity.com/paper/2110.07640/) (2021)
- [Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models](https://mlsanity.com/paper/2208.07852/) (2022)
- [Opal: Multimodal Image Generation for News Illustration](https://mlsanity.com/paper/2204.09007/) (2022)
- [PopBlends: Strategies for Conceptual Blending with Large Language Models](https://mlsanity.com/paper/2111.04920/) (2021)
- [ScatterShot: Interactive In-context Example Curation for Text Transformation](https://mlsanity.com/paper/2302.07346/) (2023)
- [Designerly Understanding: Information Needs for Model Transparency to Support Design Ideation for AI-Powered User Experience](https://mlsanity.com/paper/2302.10395/) (2023)
- [fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Tasks](https://mlsanity.com/paper/2302.11703/) (2023)

## Built on by (most cited first, 8 of 8)

- [Navigating the Unknown: A Chat-Based Collaborative Interface for Personalized Exploratory Tasks](https://mlsanity.com/paper/2410.24032/) (2024)
- [Scaffolding Recursive Divergence and Convergence in Story Ideation](https://mlsanity.com/paper/2507.03307/) (2025)
- [The role of large language models in UI/UX design: A systematic literature review](https://mlsanity.com/paper/2507.04469/) (2025)
- [Understanding User Experiences of Computer Use Agents: Design Space and Opportunities for Building Agent UX Prototypes](https://mlsanity.com/paper/2510.04452/) (2025)
- [The State of Generative AI in Software Development: Insights from Literature and a Developer Survey](https://mlsanity.com/paper/2603.16975/) (2026)
- [Facilitating Longitudinal Interaction Studies of AI Systems](https://mlsanity.com/paper/2508.10252/) (2025)
- [Imagining Design Workflows in Agentic AI Futures](https://mlsanity.com/paper/2509.20731/) (2025)
- [Auditing and Controlling AI Agent Actions in Spreadsheets](https://mlsanity.com/paper/2604.20070/) (2026)

## Abstract (arXiv)

Prototyping AI applications is notoriously difficult. While large language model (LLM) prompting has dramatically lowered the barriers to AI prototyping, designers are still prototyping AI functionality and UI separately. We investigate how coupling prompt and UI design affects designers' workflows. Grounding this research, we developed PromptInfuser, a Figma plugin that enables users to create semi-functional mockups, by connecting UI elements to the inputs and outputs of prompts. In a study with 14 designers, we compare PromptInfuser to designers' current AI-prototyping workflow. PromptInfuser was perceived to be significantly more useful for communicating product ideas, more capable of producing prototypes that realistically represent the envisioned artifact, more efficient for prototyping, and more helpful for anticipating UI issues and technical constraints. PromptInfuser encouraged iteration over prompt and UI together, which helped designers identify UI and prompt incompatibilities and reflect upon their total solution. Together, these findings inform future systems for prototyping AI applications.