Understanding Prompt Management in GitHub Repositories: A Call for Best Practices
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866908745816080384 |
|---|---|
| author | Li, Hao Masri, Hicham Cogo, Filipe R. Bangash, Abdul Ali Adams, Bram Hassan, Ahmed E. |
| author_facet | Li, Hao Masri, Hicham Cogo, Filipe R. Bangash, Abdul Ali Adams, Bram Hassan, Ahmed E. |
| contents | The rapid adoption of foundation models (e.g., large language models) has given rise to promptware, i.e., software built using natural language prompts. Effective management of prompts, such as organization and quality assurance, is essential yet challenging. In this study, we perform an empirical analysis of 24,800 open-source prompts from 92 GitHub repositories to investigate prompt management practices and quality attributes. Our findings reveal critical challenges such as considerable inconsistencies in prompt formatting, substantial internal and external prompt duplication, and frequent readability and spelling issues. Based on these findings, we provide actionable recommendations for developers to enhance the usability and maintainability of open-source prompts within the rapidly evolving promptware ecosystem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12421 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Understanding Prompt Management in GitHub Repositories: A Call for Best Practices Li, Hao Masri, Hicham Cogo, Filipe R. Bangash, Abdul Ali Adams, Bram Hassan, Ahmed E. Software Engineering Artificial Intelligence The rapid adoption of foundation models (e.g., large language models) has given rise to promptware, i.e., software built using natural language prompts. Effective management of prompts, such as organization and quality assurance, is essential yet challenging. In this study, we perform an empirical analysis of 24,800 open-source prompts from 92 GitHub repositories to investigate prompt management practices and quality attributes. Our findings reveal critical challenges such as considerable inconsistencies in prompt formatting, substantial internal and external prompt duplication, and frequent readability and spelling issues. Based on these findings, we provide actionable recommendations for developers to enhance the usability and maintainability of open-source prompts within the rapidly evolving promptware ecosystem. |
| title | Understanding Prompt Management in GitHub Repositories: A Call for Best Practices |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2509.12421 |