NirDiamant/RAG_Techniques

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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 innovation in this exciting field.

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: Document Parsing with Mistral OCR 4 (sponsored by Mistral AI), 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 | | | 2 | Foundational 🌱 | RAG with CSV Files | | | 3 | Foundational 🌱 | Reliable RAG | | | 4 | Foundational 🌱 | Optimizing Chunk Sizes | | | 5 | Foundational 🌱 | Proposition Chunking | | | 6 | Query Enhancement 🔍 | Query Transformations | | | 7 | Query Enhancement 🔍 | HyDE (Hypothetical Document Embedding) | | | 8 | Query Enhancement 🔍 | HyPE (Hypothetical Prompt Embedding) | | | 9 | Context Enrichment 📚 | Contextual Chunk Headers | | | 10 | Context Enrichment 📚 | Relevant Segment Extraction | | | 11 | Context Enrichment 📚 | Context Window Enhancement | | | 12 | Context Enrichment 📚 | Semantic Chunking | | | 13 | Context Enrichment 📚 | Contextual Compression | | | 14 | Context Enrichment 📚 | Document Augmentation | | | 15 | Advanced Retrieval 🚀 | Fusion Retrieval | | | 16 | Advanced Retrieval 🚀 | Reranking | | | 17 | Advanced Retrieval 🚀 | Multi-faceted Filtering | | | 18 | Advanced Retrieval 🚀 | Hierarchical Indices | | | 19 | Advanced Retrieval 🚀 | Dartboard Retrieval | [