NirDiamant/RAG_Techniques
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
From the publisher
README & documentation
Source previewRead the project’s overview, installation instructions and usage examples. The original README is the source of truth.
AI Agents Made Simple reached #1 in Software Development, Software Engineering and Software Design Tools on Amazon.com's Kindle Store. A decision guide to building AI agents that work: 28 chapters, 148 illustrations, no code. Get the book on Amazon → 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. [](http://makeapullrequest.com) [](https://www.linkedin.com/in/nir-diamant-759323134/) [](https://twitter.com/NirDiamantAI) [](https://www.reddit.com/r/EducationalAI/) [](https://discord.gg/cA6Aa4uyDX) [](https://github.com/sponsors/NirDiamant) 📖 Go deeper: the book RAG Made Simple - the 400-page visual companion to this repo. Amazon Bestseller in Generative AI Get it on Amazon (paperback · Kindle · free on Kindle Unlimited) → · Read Chapter 1 free We gratefully acknowledge the organizations and individuals who have made significant contributions to this project. Company Sponsors Individual Sponsors 🚀Cutting-edgeUpdates 💡ExpertInsights 🎯Top 0.1%Content [](https://europe-west1-rag-techniques-views-tracker.cloudfunctions.net/rag-techniques-tracker?notebook=main-readme&click=newsletter-subscribe-button&target=https%3A%2F%2Fnewsletter.diamant-ai.com%2F%3Fr%3D336pe4%26utmcampaign%3Dpub-share-checklist&text=Subscribe%20to%20DiamantAI%20Newsletter) Join over 50,000 AI enthusiasts getting unique cutting-edge insights and free tutorials!…
Read the full README ↗ · Preview checked 2026-11-01T09:56:08.002Z
Inside the original README — Document outline
- Advanced RAG Techniques 🚀
- Elevating Your Retrieval-Augmented Generation Systems
- Sponsors ❤️
- 📫 Stay Updated!
- Introduction
- Related Projects
- Join the community
- Key Features
- Advanced Techniques
- 🌱 Foundational RAG Techniques
- Overview 🔎
- Implementation 🛠️
Headings are captured from the source. Links open the publisher’s document, not a locally hosted copy.
Links from the README
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- Get the book on Amazon →
- Get it on Amazon (paperback · Kindle · free on Kindle Unlimited) →
- Read Chapter 1 free
- Browse every episode →
- RAG Explained: Why AI Gets Your Own Documents Wrong
- How Do You Search a Spreadsheet by Meaning?
- How Do You Know Your RAG Answer Isn't Made Up?
- Why Does RAG Return a Paragraph When You Asked for One Fact?
- Why Does RAG Miss an Answer That's Right There in Your Documents?
- Runnable Script
- HyDE for RAG: Search With a Fake Answer to Find the Real One
- HyDE: Exploring Hypothetical Document Embeddings for AI Retrieval
Documentation belongs to its respective authors. Reported project/model license: Other. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
What this repository does
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. Each technique has a detailed notebook tutorial.
Repository facts
- Owner
- NirDiamant
- Primary language
- Jupyter Notebook
- Stars
- 30,155
- Forks
- 3,695
- Open issues + pull requests
- 7
- License
- Other
- Archived
- No
- Default branch
- main
- Created
- 2024-07-13T16:08:36.000Z
- Last push
- 2026-10-31T02:48:39.000Z
Topics and intended use
Owner-supplied topics: agentic-rag, ai, embeddings, generative-ai, gpt, langchain, llama-index, llm, llms, machine-learning, nlp, openai, python, rag, retrieval-augmented-generation, semantic-search, tutorials, vector-database
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Source and freshness
Source: GitHub. Metadata observed 2026-10-31T12:19:22.904Z. Daily imports are snapshots, not real-time monitoring.
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