LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language
This addresses the problem of inefficient and non-reusable prompt design for non-AI experts, offering a structured solution that is incremental over existing scattered methods.
The authors tackled the challenge of designing high-quality prompts for LLMs by proposing LangGPT, a structured framework inspired by programming languages, which significantly enhanced LLM performance and generated higher-quality responses in experiments.
LLMs have demonstrated commendable performance across diverse domains. Nevertheless, formulating high-quality prompts to instruct LLMs proficiently 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 structured design template, incurring high learning costs and resulting in low reusability. In addition, it is not conducive to the iterative updating of prompts. Inspired by structured reusable programming languages, we propose LangGPT, a dual-layer prompt design framework as the programming language for LLMs. LangGPT has an easy-to-learn normative structure and provides an extended structure for migration and reuse. Experiments illustrate that LangGPT significantly enhances the performance of LLMs. Moreover, the case study shows that LangGPT leads LLMs to generate higher-quality responses. Furthermore, we analyzed the ease of use and reusability of LangGPT through a user survey in our online community.