{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/minstrel-structural-prompt-generation-with","title":"Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI Experts","arxiv_id":"2409.13449","date":"2024-09-20","proceeding":null,"authors":["Ming Wang","Yuanzhong Liu","Xiaoyu Liang","YiJie Huang","Daling Wang","Xiaocui Yang","Sijia Shen","Shi Feng","XiaoMing Zhang","Chaofeng Guan","Yifei Zhang"],"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. 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