Minstrel: Structural Prompt Generation with Multi-Agents Coordination
  for Non-AI Experts
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.
66f14c92caf6968847a713d1
Ming Wang
claimed_verified
2024-09-23T14:22:36.918Z
644378a96cea0db46dc96b39
https://cdn-avatars.huggingface.co/v1/production/uploads/644378a96cea0db46dc96b39/wjGNaXjfhSdWr8kM6Amai.jpeg
Ming Wang
user
sci-m-wang
66f14c92caf6968847a713d2
Yuanzhong Liu
66f14c92caf6968847a713d3
Xiaoyu Liang
66f14c92caf6968847a713d4
Yijie Huang
66f14c92caf6968847a713d5
Daling Wang
66f14c92caf6968847a713d6
Xiaocui Yang
66f14c92caf6968847a713d7
Sijia Shen
66f14c92caf6968847a713d8
Shi Feng
66f14c92caf6968847a713d9
Xiaoming Zhang
66f14c92caf6968847a713da
Chaofeng Guan
66f14c92caf6968847a713db
Yifei Zhang
Minstrel: Structural Prompt Generation with Multi-Agents Coordination
  for Non-AI Experts
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.
LangGPT and Minstrel enhance LLM performance by providing structural prompts that are more user-friendly and adaptable for non-AI experts.
LLMs prompt engineering prompt optimizers structural prompt design multi-generative agent system reflection user survey