Key Points

1. Large Language Models (LLMs) have demonstrated impressive capabilities across diverse domains, but formulating high-quality prompts to assist them remains a challenge for non-AI experts.

2. Existing research in prompt engineering suggests scattered optimization principles and empirically dependent prompt optimizers, lacking a structural design and making it difficult for non-AI experts to learn and iteratively update prompts.

3. The paper proposes LangGPT, a structural prompt design framework that combines the systematic, prescriptive, and reusable characteristics of programming language with the flexibility and extensibility of natural language.

4. The paper introduces Minstrel, a multi-generative agent system with reflection to automate the generation of structural LangGPT prompts.

5. Experiments and case studies demonstrate that structural prompts generated by Minstrel or written manually significantly enhance the performance of LLMs compared to baseline prompts.

6. The paper conducts a user survey in an online community to analyze the ease of use and user satisfaction of the structural prompts.

7. The paper invites ordinary users to design prompts, further validating the ease of use of structural prompts and verifying the performance gain effect on different LLM models.

8. The paper highlights the contributions of proposing the LangGPT framework, developing the Minstrel generation tool, and demonstrating the effectiveness of structural prompts through experiments and user studies.

9. The paper acknowledges the limitations of the evaluation relying on the Open LLM Leaderboard, and mentions plans to further optimize the design of prompts, especially for low-performance LLMs.


Summary

Research Framework and System
This research paper proposes a framework called LangGPT and a multi-generative agent system called Minstrel to address the challenges non-AI experts face in formulating high-quality prompts to assist large language models (LLMs) in their tasks.

LangGPT Structural Prompt Design Framework
The paper notes that existing prompt engineering approaches lack a structural design, making it difficult for non-experts to iteratively update prompts. LangGPT is a structural prompt design framework that combines the systematic, prescriptive, and reusable characteristics of programming language with the flexibility and extensibility of natural language. It has a dual-layer structure composed of modules and elements, where modules represent different aspects of the content requirements and elements represent specific content within the modules.

Minstrel Multi-Agent Prompt Generation Tool
Minstrel is a structural prompt generation tool based on a multi-agent system with reflection. It generates LangGPT prompts automatically through the collaboration of three working groups: the analysis group, the design group, and the test group. The analysis group activates the relevant module agents in the design group based on the user's task requirements. The design group then generates the content for each module. The test group systematically tests the effectiveness of the prompts and provides feedback to the analysis group, which then optimizes the prompts.

Experimental Results and User Survey
Experiments and case studies demonstrate that structural prompts generated by Minstrel or manually written enhance the performance of LLMs compared to baseline prompts. Additionally, a user survey in the authors' online community shows a high level of user satisfaction and ease of use with the structural prompts. The paper makes three key contributions: (1) the proposal of the LangGPT structural prompt design framework, (2) the development of the Minstrel multi-agent prompt generation tool, and (3) the analysis of the effectiveness and ease of use of structural prompts through experiments, case studies, and a user survey.

Reference: https://arxiv.org/abs/2409.13449