Sketch Then Generate: Providing Incremental User Feedback and Guiding LLM Code Generation through Language-Oriented Code Sketches
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Abstract (arXiv)
Crafting effective prompts for code generation or editing with Large Language Models (LLMs) is not an easy task. Particularly, the absence of immediate, stable feedback during prompt crafting hinders effective interaction, as users are left to mentally imagine possible outcomes until the code is generated. In response, we introduce Language-Oriented Code Sketching, an interactive approach that provides instant, incremental feedback in the form of code sketches (i.e., incomplete code outlines) during prompt crafting. This approach converts a prompt into a code sketch by leveraging the inherent linguistic structures within the prompt and applying classic natural language processing techniques. The sketch then serves as an intermediate placeholder that not only previews the intended code structure but also guides the LLM towards the desired code, thereby enhancing human-LLM interaction. We conclude by discussing the approach's applicability and future plans.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 12 of its 30 references that are in the map. First posted on arXiv 7 May 2024.
Builds on
12 of 30 references.
References · 12
- 2022Grounded Copilot: How Programmers Interact with Code-Generating Modelscited by 216
- 2022Large Language Models Meet NL2Code: A Surveycited by 135
- 2022Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programmingcited by 91
- 2023Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programmingcited by 87
- 2018A Survey on Semantic Parsingcited by 62
- 2023Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Codecited by 62
- 2023"What It Wants Me To Say": Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Modelscited by 48
- 2019Universal Dependency Parsing from Scratchcited by 46
- 2018Anaphora and Coreference Resolution: A Reviewcited by 31
- 2024How Beginning Programmers and Code LLMs (Mis)read Each Othercited by 31
- 2023CoLadder: Supporting Programmers with Hierarchical Code Generation in Multi-Level Abstractioncited by 7
- 2023CrossData: Leveraging Text-Data Connections for Authoring Data Documentscited by 6
Built on by
Citing papers per year: 2024: 5; 2025: 1; 2026: 1.
7 papers.
- 2024MarsCode Agent: AI-native Automated Bug Fixingcited by 68
- 2024WaitGPT: Monitoring and Steering Conversational LLM Agent in Data Analysis with On-the-Fly Code Visualizationcited by 38
- 2024An Empirical Study on LLM-based Agents for Automated Bug Fixingcited by 23
- 2024Substance Beats Style: Why Beginning Students Fail to Code with LLMscited by 5
- 2024Frontend Diffusion: Exploring Intent-Based User Interfaces through Abstract-to-Detailed Task Transitionscited by 3
- 2025"The Diagram is like Guardrails": Structuring GenAI-assisted Hypotheses Exploration with an Interactive Shared Representationcited by 3
- 2026AdaLens: Interactive Storyline for Monitoring and Steering Long-Running Agentic Data Analysiscited by 0
- 2026AdaLens: Interactive Storyline for Monitoring and Steering Long-Running Agentic Data Analysis18 Aug 2026
- 2025"The Diagram is like Guardrails": Structuring GenAI-assisted Hypotheses Exploration with an Interactive Shared Representation21 Mar 2025
- 2024An Empirical Study on LLM-based Agents for Automated Bug Fixing15 Nov 2024
- 2024Substance Beats Style: Why Beginning Students Fail to Code with LLMs15 Oct 2024
- 2024MarsCode Agent: AI-native Automated Bug Fixing2 Sep 2024
- 2024WaitGPT: Monitoring and Steering Conversational LLM Agent in Data Analysis with On-the-Fly Code Visualization3 Aug 2024
- 2024Frontend Diffusion: Exploring Intent-Based User Interfaces through Abstract-to-Detailed Task Transitions16 Jul 2024