  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.
