# ChatGPT Prompt Patterns for Improving Code Quality, Refactoring, Requirements Elicitation, and Software Design

arXiv: https://arxiv.org/abs/2303.07839 · HTML: https://mlsanity.com/paper/2303.07839/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 11 Mar 2023.

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

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT](https://mlsanity.com/paper/2302.11382/) (2023)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [Mathematical Capabilities of ChatGPT](https://mlsanity.com/paper/2301.13867/) (2023)
- [A Categorical Archive of ChatGPT Failures](https://mlsanity.com/paper/2302.03494/) (2023)
- [An Analysis of the Automatic Bug Fixing Performance of ChatGPT](https://mlsanity.com/paper/2301.08653/) (2023)
- [Is GitHub's Copilot as Bad as Humans at Introducing Vulnerabilities in Code?](https://mlsanity.com/paper/2204.04741/) (2022)
- [ChatGPT and Software Testing Education: Promises & Perils](https://mlsanity.com/paper/2302.03287/) (2023)
- [Conversational Automated Program Repair](https://mlsanity.com/paper/2301.13246/) (2023)
- [Applying Software Patterns to Address Interoperability in Blockchain-based Healthcare Apps](https://mlsanity.com/paper/1706.03700/) (2017)

## Built on by (most cited first, 30 of 66)

- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future](https://mlsanity.com/paper/2408.02479/) (2024)
- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era](https://mlsanity.com/paper/2304.06488/) (2023)
- [Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI](https://mlsanity.com/paper/2505.19443/) (2025)
- [Best Practices for Text Annotation with Large Language Models](https://mlsanity.com/paper/2402.05129/) (2024)
- [SOEN-101: Code Generation by Emulating Software Process Models Using Large Language Model Agents](https://mlsanity.com/paper/2403.15852/) (2024)
- [Refactoring Programs Using Large Language Models with Few-Shot Examples](https://mlsanity.com/paper/2311.11690/) (2023)
- [MARE: Multi-Agents Collaboration Framework for Requirements Engineering](https://mlsanity.com/paper/2405.03256/) (2024)
- [ChatGPT for Programming Numerical Methods](https://mlsanity.com/paper/2303.12093/) (2023)
- [PerfCodeGen: Improving Performance of LLM Generated Code with Execution Feedback](https://mlsanity.com/paper/2412.03578/) (2024)
- [Performance-Aligned LLMs for Generating Fast Code](https://mlsanity.com/paper/2404.18864/) (2024)
- [Enhancing Network Management Using Code Generated by Large Language Models](https://mlsanity.com/paper/2308.06261/) (2023)
- [Decoding ChatGPT: A Taxonomy of Existing Research, Current Challenges, and Possible Future Directions](https://mlsanity.com/paper/2307.14107/) (2023)
- [Harnessing LLMs in Curricular Design: Using GPT-4 to Support Authoring of Learning Objectives](https://mlsanity.com/paper/2306.17459/) (2023)
- [From Commit Message Generation to History-Aware Commit Message Completion](https://mlsanity.com/paper/2308.07655/) (2023)
- [LyricWhiz: Robust Multilingual Zero-shot Lyrics Transcription by Whispering to ChatGPT](https://mlsanity.com/paper/2306.17103/) (2023)
- [SocialGPT: Prompting LLMs for Social Relation Reasoning via Greedy Segment Optimization](https://mlsanity.com/paper/2410.21411/) (2024)
- [LLM-based agents for automating the enhancement of user story quality: An early report](https://mlsanity.com/paper/2403.09442/) (2024)
- [Comparing Software Developers with ChatGPT: An Empirical Investigation](https://mlsanity.com/paper/2305.11837/) (2023)
- [An Empirical Study on Using Large Language Models to Analyze Software Supply Chain Security Failures](https://mlsanity.com/paper/2308.04898/) (2023)
- [Model Generation with LLMs: From Requirements to UML Sequence Diagrams](https://mlsanity.com/paper/2404.06371/) (2024)
- [MANTRA: Enhancing Automated Method-Level Refactoring with Contextual RAG and Multi-Agent LLM Collaboration](https://mlsanity.com/paper/2503.14340/) (2025)
- [ChatGPT for PLC/DCS Control Logic Generation](https://mlsanity.com/paper/2305.15809/) (2023)
- [Knowledge Engineering using Large Language Models](https://mlsanity.com/paper/2310.00637/) (2023)
- [Enhancing Computer Programming Education with LLMs: A Study on Effective Prompt Engineering for Python Code Generation](https://mlsanity.com/paper/2407.05437/) (2024)
- [Enhancing Robustness in Large Language Models: Prompting for Mitigating the Impact of Irrelevant Information](https://mlsanity.com/paper/2408.10615/) (2024)
- [LAURA: Enhancing Code Review Generation with Context-Enriched Retrieval-Augmented LLM](https://mlsanity.com/paper/2512.01356/) (2025)
- [On the Opportunities of Green Computing: A Survey](https://mlsanity.com/paper/2311.00447/) (2023)
- [Automated User Story Generation with Test Case Specification Using Large Language Model](https://mlsanity.com/paper/2404.01558/) (2024)

## Abstract (arXiv)

This paper presents prompt design techniques for software engineering, in the form of patterns, to solve common problems when using large language models (LLMs), such as ChatGPT to automate common software engineering activities, such as ensuring code is decoupled from third-party libraries and simulating a web application API before it is implemented. This paper provides two contributions to research on using LLMs for software engineering. First, it provides a catalog of patterns for software engineering that classifies patterns according to the types of problems they solve. Second, it explores several prompt patterns that have been applied to improve requirements elicitation, rapid prototyping, code quality, refactoring, and system design.