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.

翻译：基础模型（如大语言模型）的快速普及催生了提示软件——即通过自然语言提示构建的软件系统。提示的有效管理（包括组织与质量保障）至关重要却充满挑战。本研究通过对92个GitHub仓库中24,800个开源提示进行实证分析，系统探究提示管理实践与质量属性。研究发现存在若干关键挑战：提示格式存在显著不一致性、内部与外部提示重复现象普遍、可读性与拼写问题频发。基于这些发现，我们为开发者提供可操作的建议，以提升快速演进的提示软件生态系统中开源提示的可用性与可维护性。