[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"learning-resources":3},[4,12,18,24,31,37,43,49,54,60,66,73,79,85,91,97,103,109,115,121,127],{"id":5,"slug":6,"title":7,"description":8,"level":9,"topic":10,"url":11,"content":11,"sortOrder":5},1,"part-1-llm-basics","Part 1 · 大模型基础认知","小白零基础友好。这一站不写代码，只建立\"大模型到底是什么\"的骨架。","BEGINNER","大模型认知",null,{"id":13,"slug":14,"title":15,"description":16,"level":9,"topic":17,"url":11,"content":11,"sortOrder":13},2,"part-2-llm-principles","Part 2 · 大模型原理 · 入门实操","这一站不卷数学，只把\"理解模型所需的知识\"讲透。；站 2A 解决\"理解模型\"，站 2B 解决\"面试能答\"。；这一层是\"有余力再做\"，不做也不影响你进入站 3。","大模型原理",{"id":19,"slug":20,"title":21,"description":22,"level":9,"topic":17,"url":11,"content":11,"sortOrder":23},100,"part-3-llm-principles-deep","Part 3 · 大模型原理 · 面试深入","数学基础、机器学习、神经网络、词向量、思维链等面试必考原理",3,{"id":25,"slug":26,"title":27,"description":28,"level":29,"topic":17,"url":11,"content":11,"sortOrder":30},101,"part-4-llm-principles-advanced","Part 4 · 大模型原理 · 进阶可选","从零写迷你模型、RLHF、PPO、分布式训练实操等进阶内容","INTERMEDIATE",4,{"id":23,"slug":32,"title":33,"description":34,"level":9,"topic":35,"url":11,"content":11,"sortOrder":36},"part-5-prompt-engineering","Part 5 · Prompt 提示词工程","从这里开始，你每天都要真的调大模型 API。","提示工程",5,{"id":30,"slug":38,"title":39,"description":40,"level":9,"topic":41,"url":11,"content":11,"sortOrder":42},"part-6-llm-api","Part 6 · 大模型 API 调用","站 3 你已经调通了 API。这一站把\"调用\"做成专业水准：","API 工程",6,{"id":36,"slug":44,"title":45,"description":46,"level":29,"topic":47,"url":11,"content":11,"sortOrder":48},"part-7-rag","Part 7 · RAG 检索增强生成","这一站是\"能落地\"的关键：让模型回答**你的私有知识**。","RAG",7,{"id":42,"slug":50,"title":51,"description":52,"level":29,"topic":47,"url":11,"content":11,"sortOrder":53},"part-8-rag-optimization","Part 8 · RAG 优化与评估","站 5 你做出了\"能跑\"的 RAG。这一站把它做到\"能打\"：",8,{"id":48,"slug":55,"title":56,"description":57,"level":29,"topic":58,"url":11,"content":11,"sortOrder":59},"part-9-langchain","Part 9 · LangChain 框架","目标：用 LangChain 把\"模型、提示词、检索、记忆、工具\"串成可复用的链和 Agent。","LangChain",9,{"id":53,"slug":61,"title":62,"description":63,"level":29,"topic":64,"url":11,"content":11,"sortOrder":65},"part-10-agent","Part 10 · Agent 基础","让模型从\"会聊天\"变成\"能干活\"：自己拆任务、调工具、看结果、再行动。","Agent",10,{"id":59,"slug":67,"title":68,"description":69,"level":70,"topic":71,"url":11,"content":11,"sortOrder":72},"part-11-multi-agent","Part 11 · 多 Agent 与 Agent IDE","从\"单个 Agent\"升级到\"多 Agent 协作\"，并掌握 AutoGen \u002F LangGraph \u002F GPTs \u002F Coze \u002F Dify 五个工具。","ADVANCED","多 Agent",11,{"id":65,"slug":74,"title":75,"description":76,"level":70,"topic":77,"url":11,"content":11,"sortOrder":78},"part-12-llamaindex","Part 12 · LlamaIndex","站 5~7 你用了 LangChain 做 RAG。LlamaIndex 是另一条专攻\"数据 + LLM\"的路线。","LlamaIndex",12,{"id":72,"slug":80,"title":81,"description":82,"level":70,"topic":83,"url":11,"content":11,"sortOrder":84},"part-13-transformer","Part 13 · Transformer 深入","站 2A 建立了直觉，这一站把手写代码跑通——从\"懂概念\"到\"写得出\"。","Transformer",13,{"id":78,"slug":86,"title":87,"description":88,"level":70,"topic":89,"url":11,"content":11,"sortOrder":90},"part-14-open-models","Part 14 · 开源模型与私有化部署","目标：把主流开源模型在本地\u002F服务器跑通，理解服务化工程（显存、量化、并发）。","模型部署",14,{"id":84,"slug":92,"title":93,"description":94,"level":70,"topic":95,"url":11,"content":11,"sortOrder":96},"part-15-finetuning","Part 15 · 模型微调 Fine-Tuning","目标：理解\"什么时候必须微调、怎么选基座、怎么备数据\"，并完整跑通一次业务微调。","微调",15,{"id":90,"slug":98,"title":99,"description":100,"level":70,"topic":101,"url":11,"content":11,"sortOrder":102},"part-16-peft-lora","Part 16 · PEFT 参数高效微调","重点吃透 **LoRA**：用极少量可训练参数微调大模型（消费级显卡就能跑）。","PEFT\u002FLoRA",16,{"id":96,"slug":104,"title":105,"description":106,"level":70,"topic":107,"url":11,"content":11,"sortOrder":108},"part-17-quantization","Part 17 · 模型量化","目标：理解\"为什么量化、主流算法各自思路\"，能动手量化并评估效果。","量化",17,{"id":102,"slug":110,"title":111,"description":112,"level":70,"topic":113,"url":11,"content":11,"sortOrder":114},"part-18-data-evaluation","Part 18 · 训练数据与模型评估","数据决定模型上限，评估决定你\"能不能交付\"。","数据与评估",18,{"id":108,"slug":116,"title":117,"description":118,"level":70,"topic":119,"url":11,"content":11,"sortOrder":120},"part-19-multimodal","Part 19 · 多模态应用","让 AI 同时\"看得懂图、听得了音、说得出话\"。","多模态",19,{"id":114,"slug":122,"title":123,"description":124,"level":70,"topic":125,"url":11,"content":11,"sortOrder":126},"part-20-projects-career","Part 20 · 项目实战 + 求职备战","把 0→1 到 1→100 学到的全部收敛成**能展示、能讲、能面试**的项目。","项目与求职",20,{"id":128,"slug":129,"title":130,"description":131,"level":70,"topic":132,"url":11,"content":11,"sortOrder":133},102,"extra-2026-ai-tech","额外篇 · 2026 年 AI 应用开发必学的 5 项成熟技术","从近半年爆发的技术里，挑出已经过了尝鲜期、能真正用在项目里的 5 项：推理模型、MCP、多模态、GraphRAG、AI 编程工具链，每项都给到能跑通的代码。","AI 工程实践",21]