mlsanityData to 14 Oct 2026

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

arXiv 2402.16929 ↗First posted 26 Feb 2024cs.SE cs.AI cs.CLcited by 12

On the map: LLM reasoning → Prompting and chain-of-thought reasoning

Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 33 of its 69 references that are in the map. First posted on arXiv 26 Feb 2024.

Builds on

33 of 69 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Models28 Jan

Other references · 32

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  3. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  4. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  5. 2018Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challengecited by 4,886
  6. 2019HellaSwag: Can a Machine Really Finish Your Sentence?cited by 4,100
  7. 2023Qwen Technical Reportcited by 3,344
  8. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,975
  9. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  10. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
  11. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,439
  12. 2023OpenAssistant Conversations -- Democratizing Large Language Model Alignmentcited by 721
20 more
  1. 2023Automatic Prompt Optimization with "Gradient Descent" and Beam Searchcited by 653
  2. 2024DeepSeek LLM: Scaling Open-Source Language Models with Longtermismcited by 642
  3. 2023Exploring Large Language Models for Communication Games: An Empirical Study on Werewolfcited by 241
  4. 2023PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimizationcited by 233
  5. 2023Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Biascited by 232
  6. 2022Optimizing Prompts for Text-to-Image Generationcited by 207
  7. 2019WinoGrande: An Adversarial Winograd Schema Challenge at Scalecited by 204
  8. 2023EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizerscited by 193
  9. 2023HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledgecited by 162
  10. 2023Self-Alignment with Instruction Backtranslationcited by 151
  11. 2023Black-Box Prompt Optimization: Aligning Large Language Models without Model Trainingcited by 122
  12. 2023Evaluating Large Language Models on Controlled Generation Taskscited by 73
  13. 2023Unleashing the potential of prompt engineering for large language modelscited by 72
  14. 2023A Survey of Reasoning with Foundation Modelscited by 70
  15. 2023Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4cited by 48
  16. 2023AutoHint: Automatic Prompt Optimization with Hint Generationcited by 30
  17. 2022Crosslingual Generalization through Multitask Finetuningcited by 26
  18. 2023A study on Prompt Design, Advantages and Limitations of ChatGPT for Deep Learning Program Repaircited by 26
  19. 2023Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot Learningcited by 7
  20. 2022Decorate the Examples: A Simple Method of Prompt Design for Biomedical Relation Extractioncited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 9; 2026: 1.

12 papers.

  1. 2024FlipAttack: Jailbreak LLMs via Flippingcited by 80
  2. 2025MathFusion: Enhancing Mathematical Problem-solving of LLM through Instruction Fusioncited by 30
  3. 2025Beyond Prompt Content: Enhancing LLM Performance via Content-Format Integrated Prompt Optimizationcited by 16
  4. 2024Hierarchical Retrieval-Augmented Generation Model with Rethink for Multi-hop Question Answeringcited by 14
  5. 2025TeleAntiFraud-28k: An Audio-Text Slow-Thinking Dataset for Telecom Fraud Detectioncited by 12
  6. 2025OMGM: Orchestrate Multiple Granularities and Modalities for Efficient Multimodal Retrievalcited by 10
  7. 2025What Makes a Good Natural Language Prompt?cited by 7
  8. 2025Can GPT tell us why these images are synthesized? Empowering Multimodal Large Language Models for Forensicscited by 3
  9. 2025ConSCompF: Consistency-focused Similarity Comparison Framework for Generative Large Language Modelscited by 2
  10. 2025PromptPrism: A Linguistically-Inspired Taxonomy for Promptscited by 2
  11. 2025DelvePO: Direction-Guided Self-Evolving Framework for Flexible Prompt Optimizationcited by 0
  12. 2026PRISM: Prosody-Integrated Multi-Agent Reasoning Framework for Empathetic Spoken Dialoguecited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Minstrel: Structural Prompt Generation with Multi-Agents Coordination for Non-AI Experts
  2. 2025AutoPDL: Automatic Prompt Optimization for LLM Agents
  3. 2024PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and Heuristic-based Sampling
  4. 2026The PICCO Framework for Large Language Model Prompting: A Taxonomy and Reference Architecture for Prompt Structure
  5. 2025Rethinking Prompt Optimizers: From Prompt Merits to Optimization
  6. 2025Cross-Lingual Prompt Steerability: Towards Accurate and Robust LLM Behavior across Languages
  7. 2024PRewrite: Prompt Rewriting with Reinforcement Learning
  8. 2024Self-Instructed Derived Prompt Generation Meets In-Context Learning: Unlocking New Potential of Black-Box LLMs