RAG_Techniques and Master-Retrieval-Augmented-Generation-RAG-Systems
These are complementary educational resources—one is a hands-on techniques repository for implementing RAG systems, while the other is a structured course/book codebase for learning RAG fundamentals, so they could be used together for both theoretical understanding and practical implementation patterns.
About RAG_Techniques
NirDiamant/RAG_Techniques
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
Covers advanced RAG patterns including agentic retrieval loops, hybrid search strategies (dense-sparse retrieval fusion), query optimization techniques, and multi-document reasoning—beyond basic retrieval pipelines. Implementations target popular frameworks like LangChain and LlamaIndex with code-first Jupyter notebooks, focusing on practical enhancements for production-grade systems.
About Master-Retrieval-Augmented-Generation-RAG-Systems
PacktPublishing/Master-Retrieval-Augmented-Generation-RAG-Systems
This is the code repository for Master Retrieval-Augmented Generation (RAG) Systems, published by Packt Publishing
Covers naive through advanced RAG implementations with hands-on techniques including query expansion, dense passage retrieval, and cross-encoder/bi-encoder re-ranking for optimized document retrieval. Integrates practical optimization strategies for answer generation and demonstrates how to apply these patterns within AI applications through progressive, project-based learning modules.
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