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NirDiamant/RAG_Techniques
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
About NirDiamant/RAG_Techniques
NirDiamant/RAG_Techniques is an open-source project on GitHub, mainly written in Jupyter Notebook. This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial. It currently holds 29,503 stars and 0 forks with 0 open issues, and was last pushed on an unknown date (repository created unknown).
Project Overview
AI Homed tracks it on the AI Models & LLM Tools board.
GitHub Repository Details
README
Advanced RAG Techniques 🚀
Elevating Your Retrieval-Augmented Generation Systems
A community-driven hub of 42+ runnable notebooks covering RAG techniques from foundational to cutting-edge - the intuition, the code, and the references to build more accurate, context-rich retrieval systems.
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🎓 From RAG prototypes to production
Prompt to Production - my full course on building software with AI the way professionals do: the methods and paradigms behind reliable, efficient, modular production systems, taught systematically. 17 modules, each pairing a video lecture with a hands-on lab, from your first structured prompt to a working production system.
The course is live. Every module is out, lecture and lab.
🎁 Try a full module, free
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🛠️ Hands-on tutorial |
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One npm install adds the module's AI assistant to your Claude Code, and it guides you through the tutorial as you build.
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We gratefully acknowledge the organizations and individuals who have made significant contributions to this project.
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🎯 Top 0.1% Content |
Join over 50,000 AI enthusiasts getting unique cutting-edge insights and free tutorials! Plus, subscribers get exclusive early access and special 33% discounts to my book and the upcoming RAG Techniques course!
🎬 Prefer video?
I break these ideas down into short, one-idea-per-episode explainers on YouTube.
🆕 AI Is Rewarded for Guessing [Hallucination]
why a model guesses instead of saying I don't know, watched on its own token probabilities, and where retrieval moves the fact out of the guess
RAG Explained: Why AI Gets Your Own Documents Wrong why chunks overlap, what "meaning space" is, and where simple RAG breaks down |
How Do You Search a Spreadsheet by Meaning? turn each row into one labelled line and search the table by meaning |
How Do You Know Your RAG Answer Isn't Made Up? three checkpoints that catch a bad chunk on the way in and an unsupported claim on the way out |
Why Does RAG Return a Paragraph When You Asked for One Fact? why a paragraph's embedding is a blend that points at nothing in particular |
Introduction
Retrieval-Augmented Generation (RAG) is revolutionizing the way we combine information retrieval with generative AI. This repository showcases a curated collection of advanced techniques designed to supercharge your RAG systems, enabling them to deliver more accurate, contextually relevant, and comprehensive responses.
Our goal is to provide a valuable resource for researchers and practitioners looking to push the boundaries of what's possible with RAG. By fostering a collaborative environment, we aim to accelerate innovation in this exciting field.
📖 Go deeper: the book
RAG Made Simple - the 400-page visual companion to this repo. Amazon Bestseller in Generative AI · 1,500+ readers · ⭐ 4.6
Get it on Amazon (paperback · Kindle · free on Kindle Unlimited) → · Read Chapter 1 free
Related Projects
🚀 Agents Towards Production - code-first tutorials for shipping production-grade GenAI agents, prototype to scale.
🤖 GenAI Agents - a broad collection of AI agent implementations and tutorials.
🖋️ Prompt Engineering Techniques - prompting strategies from basics to advanced.
🧠 Agent Memory Techniques - 30 notebooks on agent memory: vector stores, knowledge graphs, Mem0, MemGPT, Zep, Graphiti.
Join the community
Contributions make this better - propose ideas, share techniques, or give feedback via CONTRIBUTING.md.
r/EducationalAI · Discord · LinkedIn
Key Features
- 🧠 State-of-the-art RAG enhancements
- 📚 Comprehensive documentation for each technique
- 🛠️ Practical implementation guidelines
- 🌟 Regular updates with the latest advancements
Advanced Techniques
Explore our extensive list of cutting-edge RAG techniques:
Recently added: MemoRAG (memory-augmented retrieval), End-to-End RAG Evaluation, Open-RAG-Eval, JSON RAG. 42 notebooks and growing.
| # | Category | Technique | View |
|---|----------|-----------|------|
| 1 | Foundational 🌱 | Basic RAG |
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| 2 | Foundational 🌱 | RAG with CSV Files |
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| 3 | Foundational 🌱 | Reliable RAG |
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| 4 | Foundational 🌱 | Optimizing Chunk Sizes |
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| 5 | Foundational 🌱 | Proposition Chunking |
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| 6 | Query Enhancement 🔍 | Query Transformations |
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| 7 | Query Enhancement 🔍 | HyDE (Hypothetical Document Embedding) |
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| 8 | Query Enhancement 🔍 | HyPE (Hypothetical Prompt Embedding) |
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| 9 | Context Enrichment 📚 | Contextual Chunk Headers |
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| 10 | Context Enrichment 📚 | Relevant Segment Extraction |
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| 11 | Context Enrichment 📚 | Context Window Enhancement |
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| 12 | Context Enrichment 📚 | Semantic Chunking |
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| 13 | Context Enrichment 📚 | Contextual Compression |
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| 14 | Context Enrichment 📚 | Document Augmentation |
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| 15 | Advanced Retrieval 🚀 | Fusion Retrieval | [
](https://colab.research.google.com/
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