In recent years, the rise of Large Language Models (LLMs) has spurred a growing demand for plug-and-play AI systems. Among the various AI techniques, prompt engineering stands out as particularly significant. However, users often face challenges in writing prompts due to the steep learning curve and significant time investment, and existing automatic prompt engineering (APE) models can be difficult to use. To address this issue, we propose PAS, an LLM-based plug-and-play APE system. PAS utilizes LLMs trained on high-quality, automatically generated prompt complementary datasets, resulting in exceptional performance. In comprehensive benchmarks, PAS achieves state-of-the-art (SoTA) results compared to previous APE models, with an average improvement of 6.09 points. Moreover, PAS is highly efficient, achieving SoTA performance with only 9000 data points. Additionally, PAS can autonomously generate prompt augmentation data without requiring additional human labor. Its flexibility also allows it to be compatible with all existing LLMs and applicable to a wide range of tasks. PAS excels in human evaluations, underscoring its suitability as a plug-in for users. This combination of high performance, efficiency, and flexibility makes PAS a valuable system for enhancing the usability and effectiveness of LLMs through improved prompt engineering.

翻译：近年来，大型语言模型（LLMs）的兴起催生了市场对即插即用人工智能系统日益增长的需求。在各种人工智能技术中，提示工程尤为关键。然而，用户常因陡峭的学习曲线和大量的时间投入而在编写提示时面临挑战，且现有的自动提示工程（APE）模型往往难以使用。为解决这一问题，我们提出了PAS，一个基于LLM的即插即用APE系统。PAS利用在高质量、自动生成的提示互补数据集上训练的大型语言模型，从而实现了卓越的性能。在综合基准测试中，与以往的APE模型相比，PAS取得了最先进（SoTA）的结果，平均提升了6.09分。此外，PAS具有极高的数据效率，仅用9000个数据点就达到了SoTA性能。同时，PAS能够自主生成提示增强数据，无需额外的人工劳动。其灵活性也使其能与所有现有LLMs兼容，并适用于广泛的任务。PAS在人类评估中表现优异，凸显了其作为用户插件的适用性。这种高性能、高效率与高灵活性的结合，使PAS成为一个通过改进提示工程来提升LLMs可用性和有效性的宝贵系统。