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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.01604 |
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| _version_ | 1866908389779439616 |
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| author | DiCuffa, Sophia Zambrana, Amanda Yadav, Priyanshi Madiraju, Sashidhar Suman, Khushi AlOmar, Eman Abdullah |
| author_facet | DiCuffa, Sophia Zambrana, Amanda Yadav, Priyanshi Madiraju, Sashidhar Suman, Khushi AlOmar, Eman Abdullah |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01604 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring Prompt Patterns in AI-Assisted Code Generation: Towards Faster and More Effective Developer-AI Collaboration DiCuffa, Sophia Zambrana, Amanda Yadav, Priyanshi Madiraju, Sashidhar Suman, Khushi AlOmar, Eman Abdullah Software Engineering 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. |
| title | Exploring Prompt Patterns in AI-Assisted Code Generation: Towards Faster and More Effective Developer-AI Collaboration |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2506.01604 |