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youngyangyang04/llm-master

大模型（LLM）全栈学习路线与中文教程🔥：覆盖 Prompt Engineering、RAG、AI Agent、MCP、微调、模型部署、Transformer、AI 编程与大厂面试，从入门到生产实践。

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从第一次调用模型 API，到构建可评估的 RAG、可靠的 Agent 与可上线的 AI 系统。 [](https://github.com/youngyangyang04/llm-master/stargazers) [](https://github.com/youngyangyang04/llm-master/network/members) [](LICENSE) [](docs/README.md) [](#) 开始学习 · 全部教程 · 专题索引 · 面试题库 网上从来不缺大模型资料，缺的是一条真正适合程序员的学习路径。 LLM Master 不是零散文章的合集，而是一套从基础认知 → 应用开发 → RAG → Agent → 微调与部署 → Transformer → 项目与面试逐级展开的完整知识体系。它关心的不只是“这个概念是什么”，更关心：为什么这样设计、工程上如何落地、出了问题怎么排查、面试时如何讲清楚。 如果你有 Java、C++、Go、Python 或前端开发基础，希望进入大模型应用开发、Agent 工程或 AI 后端，这个仓库就是为你准备的。 如果需要导师带学大模型应用开发，做硬核大模型项目，破局转行进入大模型应用开发领域，可以报名「卡码大模型应用开发训练营」 [](https://programmercarl.com/xunlian/damoxing.html) 先理解大模型应用开发在做什么、不同岗位有什么区别，以及 Prompt、RAG、Agent、微调分别解决什么问题。 从“会聊天”走向“会开发”，掌握 Prompt、结构化输出、流式响应、工具调用、上下文、成本和延迟。 打通文档处理、切片、Embedding、召回、Rerank、生成、引用和评估的完整链路。 理解什么时候需要 Agent，掌握工具契约、状态、记忆、规划、权限、失败恢复和评估。 把 Demo…

youngyangyang04/llm-master on GitHub A short preview, not the full document.

Read the full README ↗ · Preview checked 2026-11-01T10:46:06.132Z

Inside the original README — Document outline
  1. LLM-Master
  2. 程序员的大模型全栈学习路线
  3. 为什么选择 LLM Master
  4. 大模型开发知识全景
  5. 导师带学
  6. 学习路线
  7. 阶段 0：建立全局认知
  8. 阶段 1：掌握模型调用基本功
  9. 阶段 2：构建可评估的 RAG
  10. 阶段 3：从 Workflow 走向 Agent
  11. 阶段 4：补齐生产工程能力
  12. 阶段 5：理解原理，准备面试

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What this repository does

大模型（LLM）全栈学习路线与中文教程🔥：覆盖 Prompt Engineering、RAG、AI Agent、MCP、微调、模型部署、Transformer、AI 编程与大厂面试，从入门到生产实践。

Repository facts

Owner
youngyangyang04
Primary language
Not reported
Stars
1,076
Forks
107
Open issues + pull requests
4
License
MIT
Archived
No
Default branch
main
Created
2026-09-12T07:33:31.000Z
Last push
2026-09-19T08:45:46.000Z

Topics and intended use

Owner-supplied topics: agent, large-language-models, llm, llm-learning, llm-tutorial, rag

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