title: Minstrel: Structural Prompt Generation with Multi-Agents Coordination
    for Non-AI Experts
summary: 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.
paper_authors._id: 66f14c92caf6968847a713d1
paper_authors.name: Ming Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-23T14:22:36.918Z
paper_authors._id: 644378a96cea0db46dc96b39
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/644378a96cea0db46dc96b39/wjGNaXjfhSdWr8kM6Amai.jpeg
paper_authors.fullname: Ming Wang
paper_authors.type: user
paper_authors.user: sci-m-wang
paper_authors._id: 66f14c92caf6968847a713d2
paper_authors.name: Yuanzhong Liu
paper_authors._id: 66f14c92caf6968847a713d3
paper_authors.name: Xiaoyu Liang
paper_authors._id: 66f14c92caf6968847a713d4
paper_authors.name: Yijie Huang
paper_authors._id: 66f14c92caf6968847a713d5
paper_authors.name: Daling Wang
paper_authors._id: 66f14c92caf6968847a713d6
paper_authors.name: Xiaocui Yang
paper_authors._id: 66f14c92caf6968847a713d7
paper_authors.name: Sijia Shen
paper_authors._id: 66f14c92caf6968847a713d8
paper_authors.name: Shi Feng
paper_authors._id: 66f14c92caf6968847a713d9
paper_authors.name: Xiaoming Zhang
paper_authors._id: 66f14c92caf6968847a713da
paper_authors.name: Chaofeng Guan
paper_authors._id: 66f14c92caf6968847a713db
paper_authors.name: Yifei Zhang
paper_title: Minstrel: Structural Prompt Generation with Multi-Agents Coordination
    for Non-AI Experts
paper_summary: 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.
paper_ai_keywords: LangGPT Minstrel multi-generative agent system reflection structural prompts