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Foundation
Core concepts behind RAG — what it is, why it works, and when to use it.
Embeddings
Turn text and images into vectors that capture semantic meaning.
Chunking
Split documents intelligently so every chunk carries coherent meaning.
Vector Databases
Stores built for fast nearest-neighbor search over millions of embeddings.
Retrieval
Query strategies from similarity search to hybrid and metadata-aware retrieval.
Advanced RAG
Reranking, agentic RAG, Graph RAG, evaluation, and production architecture.