Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is the dominant architecture for building AI systems that answer questions based on specific knowledge sources rather than relying solely on pre-trained model weights. This category covers every component of a RAG pipeline — vector databases, embeddings, chunking strategies, reranking, and context injection — as well as advanced patterns like hybrid search and agentic RAG. Mastering these terms is essential for anyone building production-ready AI assistants.

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