LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs
  from the Programming Language
Ming Wang Yuanzhong Liu Xiaoming Zhang Songlian Li Yijie Huang Chi Zhang Daling Wang Shi Feng Jigang Li
LLMs have demonstrated commendable performance across diverse domains.
Nevertheless, formulating high-quality prompts to effectively instruct LLMs
poses a challenge for non-AI experts. Existing research in prompt engineering
suggests somewhat fragmented 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. 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 capacity of LLMs to produce responses of superior
quality compared to baselines. Moreover, LangGPT has proven effective in
guiding LLMs to generate high-quality prompts. We have built a community on
LangGPT to facilitate the tuition and sharing of prompt design. We also
analyzed the ease of use and reusability of LangGPT through a community user
survey.