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LangGPT is a structured, reusable prompt design framework that enables anyone to create high-quality prompts for Large Language Models. Think of it as a "programming language for prompts" — systematic, template-based, and infinitely scalable.
Traditional prompt engineering relies on scattered tips and trial-and-error. LangGPT transforms this chaos into a structured methodology:
Academic Foundation: Published research at arXiv:2402.16929 | 中文版
Let AI create prompts for you:
Basic LangGPT structure:
# Role: Your_Role_Name ## Profile - Author: YourName - Version: 1.0 - Language: English - Description: Clear role description and core capabilities ## Goal - Outcome: What concrete result/outcome should be delivered for the user/session - Done Criteria: Clear acceptance criteria (how we know it’s finished and good) - Non-Goals: What is explicitly out of scope to avoid scope creep ### Skill-1 1. Specific skill description 2. Expected behavior and output ## Rules 1. Don't break character under any circumstance 2. Don't make up facts or hallucinate ## Workflow 1. Analyze user input and identify intent 2. Apply relevant skills systematically 3. Deliver structured, actionable output ## Initialization As a/an <Role>, you must follow the <Rules>, you must talk to user in default <Language>, you must greet the user. Then introduce yourself and introduce the <Workflow>.
Prerequisites: Basic Markdown knowledge (Quick Guide) | GPT-4 or Claude recommended
Explore our example library and adapt proven templates to your needs.
If you use Claude Code, install the LangGPT Skill to get structured prompt writing capabilities:
Installation:
~/.claude/skills//langgptSkill Features:
Before diving into tactics, understand the principles. These essays explore the philosophy behind effective prompting:
These foundational insights will transform how you think about prompts.
Define AI personas through clear, modular sections:
| Section | Purpose | Example |
|---|---|---|
| Role | Role name/title | "逻辑学家" / "Expert Analyst" / "FitnessGPT" |
| Profile | Identity and capabilities | "Expert Python developer with 10 years experience" |
| Goal | Desired outcome, done criteria, and non-goals for this session/task | “Refactor a prompt into a reusable template; acceptance criteria: pass three structured checks; non-goal: rewriting the business logic.” |
| Skills | Specific abilities | "Debug complex code, optimize performance" |
| Rules | Boundaries and constraints | "Never execute destructive commands" |
| Workflow | Interaction logic | "1. Analyze → 2. Plan → 3. Execute" |
| Initialization | Opening message and setup | "As a |
Use
<Variable>As a <Role>, you must follow <Rules> and communicate in <Language>
This creates self-referential prompts that maintain consistency across complex instructions.
Define reusable actions for better UX:
## Commands - Prefix: "/" - Commands: - help: Display all available commands - continue: Resume interrupted output - improve: Enhance current response with deeper analysis
Add intelligence to your prompts:
If user provides [code], then analyze and suggest improvements Else if user asks [question], then provide detailed explanation Else, prompt for clarification
Reminders — Combat context loss in long conversations:
## Reminder 1. Always check role settings before responding 2. Current language: <Language>, Active rules: <Rules>
Alternative Formats — Use JSON/YAML when markdown isn't ideal:
role: DataAnalyst profile: version: "2.0" language: "Python" skills: - statistical_analysis - data_visualization
| Prompt | Description | Link |
|---|---|---|
| 🎯 FitnessGPT | Personalized diet and workout planner | View |
| 💻 Code Master CAN | Advanced coding assistant with debugging expertise | View |
| ✍️ Xiaohongshu Writer | Viral social media content generator | View |
| 🎨 Chinese Poet | Classical poetry composer in traditional styles | View |
| Resource | Description | Date |
|---|---|---|
| Academic Paper | LangGPT: Rethinking Structured Reusable Prompt Design (中文) | Feb 2024 |
| Structured Prompts Guide | Comprehensive tutorial on building high-performance prompts | Jul 2023 |
| Prompt Chains | Multi-prompt collaboration and task decomposition strategies | Aug 2023 |
| Video Tutorial | BiliBili walkthrough (by AIGCLINK) | Sep 2023 |
Feishu Knowledge Base — Curated resources, templates, and community contributions
| Project | Description | Stars |
|---|---|---|
| LangGPT | Core framework and methodology | |
| PromptVer | Semantic versioning for prompts — version control like Git | |
| PromptShow | Create beautiful prompt images (Try it) | |
| Minstrel | Multi-agent system for auto-generating prompts |
Rather than writing prompts as procedures, write the persona. Writing prompts as procedures gives the model steps and tools. Writing prompts as a persona gives the model a worldview, motivations, a value system, and a preference profile. Below are prompts that Yunzhong Jiangshu wrote while studying some well-known figures.
Curated, optimized prompts for different AI models:
| Collection | Target Model | Stars |
|---|---|---|
| wonderful-prompts | ChatGPT (Chinese) | |
| awesome-claude-prompts | Anthropic Claude | |
| awesome-deepseek-prompts | DeepSeek & R1 | |
| awesome-gemini-prompts | Google Gemini | |
| awesome-grok-prompts | xAI Grok | |
| qwen-prompts | Alibaba Qwen | |
| awesome-llama-prompts | Meta Llama 2/3 | |
| awesome-doubao-prompts | ByteDance Doubao | |
| awesome-system-prompts | System prompts from AI tools |
| Repository | Focus Area | Stars |
|---|---|---|
| Awesome-Multimodal-Prompts | GPT-4V, DALL-E 3, image/video prompts | |
| deep-research-prompts | Deep research across models | |
| awesome-voice-prompts | Voice AI and conversational agents | |
| GraphRAG-Prompts | Graph-based retrieval prompts | |
| LLM-Jailbreaks | Security research and defenses |
| Project | Description | Stars |
|---|---|---|
| BookAI | AI-powered book generation | |
| AI-Resume | Beautiful resumes with Claude Artifacts |
Transform ChatGPT with these specialized assistants:
| GPT | Purpose | Link |
|---|---|---|
| 🎯 LangGPT Expert | Auto-generate structured prompts | Launch |
| ✍️ PromptGPT | Professional prompt engineer | Launch |
| 🧠 SmartGPT-5 | Never lazy, always diligent assistant | Launch |
| 💻 Coding Expert | Comprehensive programming assistant | Launch |
| 📊 Data Table GPT | Transform messy data into clean tables | Launch |
| 🔥 PytorchGPT | PyTorch code specialist | Launch |
| 🎨 LogoGPT | Professional logo designer | Launch |
| 📄 PDF Reader | Deep document analysis and extraction | Launch |
| 🏅 MathGPT | Precise mathematical problem solver | Launch |
| 📝 WriteGPT | Professional writing across industries | Launch |
| 🎙️ 时事热评员 | Current events commentator | Launch |
| 🎀 翻译大小姐 | Elegant Chinese translations | Launch |
We welcome all contributions to make LangGPT better!
New to GitHub contributions? Check out this GitHub Minimal Contribution Guide
If you use LangGPT in research or projects, please cite:
@misc{wang2024langgpt, title={LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language}, author={Ming Wang and Yuanzhong Liu and Xiaoming Zhang and Songlian Li and Yijie Huang and Chi Zhang and Daling Wang and Shi Feng and Jigang Li}, year={2024}, eprint={2402.16929}, archivePrefix={arXiv}, primaryClass={cs.SE} }
LangGPT was inspired by excellent projects:
We're proud to see LangGPT principles applied in the wild:
云中江树 (Yun Zhong Jiang Shu)
Made with ❤️ by the langgptai Community
Empowering everyone to become a prompt expert 🚀