Exploring Prompt Patterns in AI-Assisted Code Generation: Towards Faster and More Effective Developer-AI Collaboration

• URL: http://arxiv.org/abs/2506.01604v1
• Date: Mon, 02 Jun 2025 12:43:08 GMT
• Title: Exploring Prompt Patterns in AI-Assisted Code Generation: Towards Faster and More Effective Developer-AI Collaboration
• Authors: Sophia DiCuffa, Amanda Zambrana, Priyanshi Yadav, Sashidhar Madiraju, Khushi Suman, Eman Abdullah AlOmar,
• Abstract summary: This paper explores the application of structured prompt patterns to minimize the number of interactions required for satisfactory AI-assisted code generation.<n>We analyzed seven distinct prompt patterns to evaluate their effectiveness in reducing back-and-forth communication between developers and AI.
• Score: 3.1861081539404137
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: The growing integration of AI tools in software development, particularly Large Language Models (LLMs) such as ChatGPT, has revolutionized how developers approach coding tasks. However, achieving high-quality code often requires iterative interactions, which can be time-consuming and inefficient. This paper explores the application of structured prompt patterns to minimize the number of interactions required for satisfactory AI-assisted code generation. Using the DevGPT dataset, we analyzed seven distinct prompt patterns to evaluate their effectiveness in reducing back-and-forth communication between developers and AI. Our findings highlight patterns such as ''Context and Instruction'' and ''Recipe'' as particularly effective in achieving high-quality outputs with minimal iterations. The study emphasizes the potential for prompt engineering to streamline developer-AI collaboration, providing practical insights into crafting prompts that balance precision, efficiency, and clarity.

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