GUIDE: LLM-Driven GUI Generation Decomposition for Automated Prototyping
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Abstract (arXiv)
GUI prototyping serves as one of the most valuable techniques for enhancing the elicitation of requirements and facilitating the visualization and refinement of customer needs. While GUI prototyping has a positive impact on the software development process, it simultaneously demands significant effort and resources. The emergence of Large Language Models (LLMs) with their impressive code generation capabilities offers a promising approach for automating GUI prototyping. Despite their potential, there is a gap between current LLM-based prototyping solutions and traditional user-based GUI prototyping approaches which provide visual representations of the GUI prototypes and direct editing functionality. In contrast, LLMs and related generative approaches merely produce text sequences or non-editable image output, which lacks both mentioned aspects and therefore impede supporting GUI prototyping. Moreover, minor changes requested by the user typically lead to an inefficient regeneration of the entire GUI prototype when using LLMs directly. In this work, we propose GUIDE, a novel LLM-driven GUI generation decomposition approach seamlessly integrated into the popular prototyping framework Figma. Our approach initially decomposes high-level GUI descriptions into fine-granular GUI requirements, which are subsequently translated into Material Design GUI prototypes, enabling higher controllability and more efficient adaption of changes. To efficiently conduct prompting-based generation of Material Design GUI prototypes, we propose a retrieval-augmented generation approach to integrate the component library. Our preliminary evaluation demonstrates the effectiveness of GUIDE in bridging the gap between LLM generation capabilities and traditional GUI prototyping workflows, offering a more effective and controlled user-based approach to LLM-driven GUI prototyping. Video: https://youtu.be/C9RbhMxqpTU
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Feb 2025.
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Citing papers per year: 2025: 1; 2026: 3.
4 papers.
- 2026GUI-GenBench: Evaluating Image Generation Models as Interactive GUI Environmentscited by 5
- 2025JSON Whisperer: Efficient JSON Editing with LLMscited by 2
- 2026GameUIAgent: An LLM-Powered Framework for Automated Game UI Design with Structured Intermediate Representationcited by 1
- 2026SchemaGUI: A Schema-Driven Benchmark for Controllable GUI Generation Evaluationcited by 0
- 2026SchemaGUI: A Schema-Driven Benchmark for Controllable GUI Generation Evaluation23 Aug 2026
- 2026GameUIAgent: An LLM-Powered Framework for Automated Game UI Design with Structured Intermediate Representation16 Mar 2026
- 2026GUI-GenBench: Evaluating Image Generation Models as Interactive GUI Environments9 Feb 2026
- 2025JSON Whisperer: Efficient JSON Editing with LLMs6 Oct 2025
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- 2024Zero-Shot Prompting Approaches for LLM-based Graphical User Interface Generation
- 2026From Natural Language Requirements to Graphical User Interfaces: Automated Prototyping and Verification with Pretrained Language Models ↗
- 2026Qualitative Evaluation of LLM-Designed GUI
- 2025Towards Advancing Code Generation with Large Language Models: A Research Roadmap
- 2024UICoder: Finetuning Large Language Models to Generate User Interface Code through Automated Feedback
- 2023CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets
- 2026PlayCoder: Making LLM-Generated GUI Code Playable