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）的兴起推动了对即插即用人工智能系统的日益增长的需求。在各种人工智能技术中，提示工程显得尤为重要。然而，由于陡峭的学习曲线和大量的时间投入，用户常常在编写提示时面临挑战，而现有的自动提示工程（APE）模型可能难以使用。为了解决这个问题，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LLMs，从而实现了卓越的性能。在综合基准测试中，与之前的APE模型相比，PAS取得了最先进的（SoTA）结果，平均提升了6.09分。此外，PAS具有很高的效率，仅用9000个数据点就实现了SoTA性能。另外，PAS能够自主生成提示增强数据，无需额外的人工劳动。其灵活性也使其能够兼容所有现有的LLMs，并适用于广泛的任务。PAS在人工评估中表现出色，突显了其作为用户插件的适用性。这种高性能、高效率和灵活性的结合，使得PAS成为一个通过改进提示工程来增强LLMs可用性和有效性的宝贵系统。