让非专家也能轻松设计高效提示词，自动生成结构化提示。
Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI Experts
- 用多智能体协作+反思机制自动生成结构化提示
- 实验证明结构化提示显著提升大模型表现
- 特别适合不懂AI的普通用户快速上手
大语言模型在多个领域表现优异，但非专业用户难以设计高质量提示。现有提示工程方法缺乏系统性设计，优化原则零散，依赖经验，学习成本高，不支持提示的迭代更新。受可复用编程语言启发，本文提出LangGPT结构化提示设计框架，并构建Minstrel——一个具备自我反思能力的多生成智能体系统，用于自动化生成结构化提示。实验与案例研究显示，由Minstrel生成或人工编写的结构化提示均能显著提升大模型性能。通过在线社区用户调查，进一步验证了结构化提示的易用性。
原文摘要 · Abstract (English)
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.
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