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arXiv · 2506.01604

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

Sophia DiCuffa·Amanda Zambrana·Priyanshi Yadav·Sashidhar Madiraju·Khushi Suman·Eman Abdullah AlOmar
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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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Sophia DiCuffa, Amanda Zambrana, Priyanshi Yadav, Sashidhar Madiraju, Khushi Suman, Eman Abdullah AlOmar. 2025-06-02. Exploring Prompt Patterns in AI-Assisted Code Generation: Towards Faster and More Effective Developer-AI Collaboration. https://arxiv.org/abs/2506.01604

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