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LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language

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arxiv 2402.16929 v2 pith:4LCRBMIE submitted 2024-02-26 cs.SE cs.AIcs.CLcs.PL

Ming Wang , Yuanzhong Liu , Xiaoyu Liang , Songlian Li , Yijie Huang , Xiaoming Zhang , Sijia Shen , Chaofeng Guan
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Daling Wang Shi Feng Huaiwen Zhang Yifei Zhang Minghui Zheng Chi Zhang
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classification cs.SEcs.AIcs.CLcs.PL
keywords langgptllmspromptdesignprogrammingstructuredframeworklanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 5 Pith papers

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  1. TSCG: Deterministic Tool-Schema Compilation for Agentic LLM Deployments

    cs.SE 2026-05 unverdicted novelty 7.0 of 10

    TSCG compiles JSON tool schemas into token-efficient structured text, raising tool-use accuracy for small LLMs from 0% to 84.4% on benchmarks while cutting tokens by 52-57%.

  2. Prompt Orchestration Markup Language

    cs.HC 2025-08 conditional novelty 6.0 of 10

    POML is a markup language that structures LLM prompts, embeds multimodal data, and decouples formatting via stylesheets, with case studies showing strong prompt format sensitivity.

  3. OOPrompt: Reifying Intents into Structured Artifacts for Modular and Iterative Prompting

    cs.HC 2026-04 unverdicted novelty 5.0 of 10

    OOPrompt reifies user intents into structured manipulable artifacts to enable modular and iterative prompting in LLM-based interactive systems.

  4. The PICCO Framework for Large Language Model Prompting: A Taxonomy and Reference Architecture for Prompt Structure

    cs.CL 2026-04 accept novelty 5.0 of 10

    PICCO is a five-element reference architecture (Persona, Instructions, Context, Constraints, Output) for structuring LLM prompts, derived from synthesizing prior frameworks along with a taxonomy distinguishing prompt ...

  5. PRISM: Prosody-Integrated Multi-Agent Reasoning Framework for Empathetic Spoken Dialogue

    cs.CL 2026-06 unverdicted novelty 3.0 of 10

    PRISM proposes a multi-agent system decoupling speech-to-prosody handling, LLM-based response generation, and synthesis, reporting metric improvements in empathy and prosodic fit for spoken dialogue.

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