Prompting in Practice: Investigating Software Practitioners' Use of Generative AI Tools
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918300781379584 |
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| author | Otten, Daniel Stalnaker, Trevor Wintersgill, Nathan Chaparro, Oscar Poshyvanyk, Denys |
| author_facet | Otten, Daniel Stalnaker, Trevor Wintersgill, Nathan Chaparro, Oscar Poshyvanyk, Denys |
| contents | The use of generative AI (GenAI) tools has fundamentally transformed software development. Central to this shift is prompt engineering, the practice of crafting textual prompts to guide GenAI tools in generating useful content. Although prompt engineering has emerged as a critical skill, prior research has focused primarily on cataloging of prompting techniques, with limited attention to how software practitioners employ GenAI within real-world development workflows. To address this gap, this study presents a systematic investigation of practitioners' integration of GenAI tools into software development, drawing on a rigorous survey that examines prompting strategies, conversation patterns, and reliability assessments across core software development tasks.
We surveyed 72 software practitioners who actively use GenAI to characterize AI usage patterns throughout the development process. By combining qualitative and quantitative analyses of the survey responses, we identified 13 key findings that describe how prompting is performed in practice. Our study shows that while code generation is nearly universal, proficiency strongly correlates with the use of GenAI for more nuanced tasks such as debugging and code review. Practitioners also tend to favor iterative multi-turn conversations to single-shot prompting. Documentation tasks are perceived as most reliable, while complex code generation and debugging remain major challenges. Our findings provide an empirical view of practitioner practices, ranging from basic code generation to deeper integration of GenAI into development workflows, enabling us to offer recommendations for improving both GenAI tools and the ways practitioners interact with them. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06000 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prompting in Practice: Investigating Software Practitioners' Use of Generative AI Tools Otten, Daniel Stalnaker, Trevor Wintersgill, Nathan Chaparro, Oscar Poshyvanyk, Denys Software Engineering The use of generative AI (GenAI) tools has fundamentally transformed software development. Central to this shift is prompt engineering, the practice of crafting textual prompts to guide GenAI tools in generating useful content. Although prompt engineering has emerged as a critical skill, prior research has focused primarily on cataloging of prompting techniques, with limited attention to how software practitioners employ GenAI within real-world development workflows. To address this gap, this study presents a systematic investigation of practitioners' integration of GenAI tools into software development, drawing on a rigorous survey that examines prompting strategies, conversation patterns, and reliability assessments across core software development tasks. We surveyed 72 software practitioners who actively use GenAI to characterize AI usage patterns throughout the development process. By combining qualitative and quantitative analyses of the survey responses, we identified 13 key findings that describe how prompting is performed in practice. Our study shows that while code generation is nearly universal, proficiency strongly correlates with the use of GenAI for more nuanced tasks such as debugging and code review. Practitioners also tend to favor iterative multi-turn conversations to single-shot prompting. Documentation tasks are perceived as most reliable, while complex code generation and debugging remain major challenges. Our findings provide an empirical view of practitioner practices, ranging from basic code generation to deeper integration of GenAI into development workflows, enabling us to offer recommendations for improving both GenAI tools and the ways practitioners interact with them. |
| title | Prompting in Practice: Investigating Software Practitioners' Use of Generative AI Tools |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2510.06000 |