Minstrel: Structural Prompt Generation with Multi-Agents Coordination
  for Non-AI Experts
Authors: name: Ming Wang; name: Yuanzhong Liu; name: Xiaoyu Liang; name: Yijie Huang; name: Daling Wang; name: Xiaocui Yang; name: Sijia Shen; name: Shi Feng; name: Xiaoming Zhang; name: Chaofeng Guan; name: Yifei Zhang
Published: 2024-09-20 12:30:03

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.