Master Retrieval-Augmented Generation from fundamentals to production. Learn vector databases, chunking strategies, query enhancement, hybrid search, reranking, Graph RAG, RAPTOR, Self-RAG, CRAG, and production deployment. Based on the RAG Techniques repository (25.9k stars).
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Understand the core concepts of Retrieval-Augmented Generation — vector databases, embeddings, the basic RAG pipeline, chunking strategies, and evaluation metrics.
Transform user queries for better retrieval — using HyDE, multi-query expansion, intelligent routing, and step-back prompting techniques.
Master advanced retrieval techniques — fusion retrieval, reranking, hierarchical strategies, multi-modal RAG, and contextual compression.
Enrich retrieved context with techniques like contextual chunk headers, semantic chunking, parent-child retrieval, sentence window retrieval, and document augmentation.
Explore cutting-edge RAG architectures — Graph RAG with knowledge graphs, RAPTOR recursive summarization, Self-RAG, Corrective RAG, and Agentic RAG.
Ship RAG to production with pipeline optimization, evaluation frameworks, caching, monitoring, and scaling strategies.