Minstrel: Structural Prompt Generation with Multi-Agents Coordination
  for Non-AI Experts

Ming Wang; Yuanzhong Liu; Xiaoyu Liang; Yijie Huang; Daling Wang; Xiaocui Yang; Sijia Shen; Shi Feng; Xiaoming Zhang; Chaofeng Guan; Yifei Zhang

Paper · arXiv 2409.13449

Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI Experts

Ming Wang, Yuanzhong Liu, Xiaoyu Liang, Yijie Huang, Daling Wang, Xiaocui Yang, Sijia Shen, Shi Feng, Xiaoming Zhang, Chaofeng Guan, Yifei Zhang

12 upvotesSeptember 20, 2024arXiv 预印本

AI 摘要

LangGPT and Minstrel enhance LLM performance by providing structural prompts that are more user-friendly and adaptable for non-AI experts.

LLMsprompt engineeringprompt optimizersstructural prompt designmulti-generative agent systemreflectionuser survey

Abstract

LLMs have demonstrated commendable performance across diverse domains. Nevertheless, formulating high-quality prompts to assist them in their work poses a challenge for non-AI experts. Existing research in prompt engineering suggests somewhat scattered optimization principles and designs empirically dependent prompt optimizers. Unfortunately, these endeavors lack a structural design, incurring high learning costs and it is not conducive to the iterative updating of prompts, especially for non-AI experts. Inspired by structured reusable programming languages, we propose LangGPT, a structural prompt design framework. Furthermore, we introduce Minstrel, a multi-generative agent system with reflection to automate the generation of structural prompts. Experiments and the case study illustrate that structural prompts generated by Minstrel or written manually significantly enhance the performance of LLMs. Furthermore, we analyze the ease of use of structural prompts through a user survey in our online community.