PAS: Plug-and-Play Prompt Augmentation System
Miao Zheng, Liang Hao, Fan Yang, Bin Cui, Zenan Zhou, Wentao Zhang · 2025
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 augmentation 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. Moreover, we deployed PAS for Baichuan online model, and then tested PAS using internal human evaluations in Baichuan underscoring its strong performance. This combination of high performance, efficiency, and flexibility makes PAS a valuable system for enhancing the usability and effectiveness of LLMs through automatic prompt engineering. The codebase is available at https://github.com/PKU-Baichuan-MLSystemLab/PAS.