RAG education / tutorials
A community-driven hub of 42+ runnable Jupyter notebooks teaching advanced retrieval-augmented generation (RAG) techniques, from foundational to cutting-edge, with intuition, code, and references.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
Choose this if you want a permissively-licensed (MIT) cookbook collection of RAG technique notebooks rather than the seed's NOASSERTION license.
This repository contains various advanced techniques for Retrieval-Augmented Generation (RAG) systems.
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Choose this if you want a LangChain-centric RAG tutorial with frequent updates instead of a tool-agnostic survey.
Everything you need to know to build your own RAG application
Choose this if you want MongoDB-based RAG and agent examples with broader GenAI use cases instead of general technique walkthroughs.
MongoDB's Generative AI Showcase: an exhaustive collection of examples and sample applications covering Retrieval-Augmented Generation (RAG), AI agents, and industry-specific use cases.
Choose this if you want a broader, actively updated AI engineering tutorial hub covering RAG alongside LLM and agent topics.
In-depth tutorials on LLMs, RAGs and real-world AI agent applications.
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Choose this if you only need a focused agentic RAG with LangGraph example, not the full RAG technique spectrum.
A modular Agentic RAG built with LangGraph — learn Retrieval-Augmented Generation Agents in minutes.
These projects were analysed and named RAG_Techniques among their alternatives. The relationship is not symmetric — how RAG_Techniques rates them is a separate judgement, made when RAG_Techniques is analysed in its own right.
calls RAG_Techniques “Choose it for a comprehensive set of advanced RAG technique notebooks.”
calls RAG_Techniques “When you want focused tutorials on advanced RAG techniques only.”
🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
calls RAG_Techniques “A notebook collection of advanced RAG techniques, narrower than a full RAG curriculum.”
🔍大模型应用开发实战一：RAG 技术全栈指南，在线阅读地址：https://datawhalechina.github.io/all-in-rag/
calls RAG_Techniques “Choose it if you only need a focused collection of RAG technique notebooks rather than a full agent curriculum.”
A systematic AI Agent development tutorial covering LLM agents, RAG, tool use, memory systems, multi-agent systems, LangChain, LangGraph, MCP, and agentic RL.｜从零开始学 AI Agent 开发 | 系统、全面、实战导向的 Agent 开发教程 | 每日自动追踪 arXiv 最新论文 | Learn AI Agent Development from Scratch