NirDiamant / RAG_Techniques

# 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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 🎬 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 
   **Browse every episode →**
## 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