[Paper Review] LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language

Ming Wang, Yuanzhong Liu+12 more|arXiv (Cornell University)|Feb 26, 2024

Software Testing and Debugging Techniques4 citations

TL;DR

LangGPT proposes a dual-layer, programming language-inspired framework for structured, reusable prompt design in LLMs, enabling non-AI experts to create high-quality prompts through a systematic, extensible template. Experiments show LangGPT outperforms baselines like Instruction-only and CRISPE in generating superior response quality across diverse tasks, with user surveys confirming high ease of use and reusability.

ABSTRACT

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.

Motivation & Objective

• To address the lack of systematic, reusable prompt design for non-AI experts in LLM applications.
• To reduce learning costs and improve reusability of high-quality prompts through a structured framework.
• To enable LLMs to generate high-quality prompts themselves using the proposed framework.
• To evaluate the usability and reusability of the framework through community-based user surveys.

Proposed method

• Design a dual-layer prompt structure inspired by object-oriented programming, with a normative base layer and an extensible layer for customization.
• Define a formalized syntax and semantic rules for prompt components, enabling systematic construction and reuse.
• Integrate the framework into a community platform to support collaborative prompt sharing and tuition.
• Use LLMs to generate prompts under LangGPT’s structure, improving safety and quality through structured constraints.
• Apply beam search and gradient-like editing techniques to optimize prompts within the LangGPT framework.
• Conduct controlled experiments comparing LangGPT with Instruction-only and CRISPE baselines across writing and role-playing scenarios.

Experimental results

Research questions

• RQ1 Can a structured, reusable prompt framework reduce the learning barrier for non-AI experts in prompt engineering?
• RQ2 How does LangGPT compare to existing prompt design methods (e.g., CRISPE, instruction-only) in generating high-quality LLM responses?
• RQ3 To what extent can LangGPT guide LLMs to generate high-quality, safe, and contextually appropriate prompts autonomously?
• RQ4 How does the community adoption of LangGPT reflect its ease of use and reusability in real-world settings?

Key findings

• LangGPT significantly outperformed Instruction-only and CRISPE prompts in generating high-quality responses, with average scores of 4.20 (Gemini Pro) and 4.43 (Gemini Pro) in the role-playing scenario.
• In the writing scenario, LangGPT achieved an average score of 4.20 on content richness and 4.30 on textual continuity, surpassing baselines across multiple LLMs.
• 87.81% of community users rated LangGPT’s ease of use at 3 or higher on a 5-point scale, with an overall satisfaction score of 8.48 out of 10.
• LangGPT successfully guided LLMs to generate harmless, high-quality prompts even for sensitive topics like MBTI assessment, where direct prompts were rejected.
• LangGPT enabled LLMs to maintain character consistency and depth in role-playing, such as in a 'boot-licker' persona, outperforming baselines in expressiveness and relevance.
• The framework demonstrated robustness across diverse LLMs, including GPT-3.5, Qwen, Yi, and Baichuan2, across both instruction-following and creative generation tasks.