What is Retrieval-Augmented Generation (RAG) architecture?

RAG combines vector search retrieval with LLM generation for grounded responses.

RAG systems embed documents into vector space, retrieve top-k relevant chunks using similarity search, and inject them into LLM context. This reduces hallucination and improves factual grounding. Key components include embedding models, vector databases, chunking strategies, rerankers, and prompt templates.