What is RAG (Retrieval-Augmented Generation)?
RAG is a technique that lets an LLM answer questions using your private documents by retrieving relevant chunks before generating the response.
Definition
Retrieval-Augmented Generation (RAG) is the standard production pattern for letting an LLM answer questions using your data — wikis, PDFs, support tickets, code — without retraining the model. The flow: documents are split into chunks, embedded as numerical vectors, stored in a vector database (Pinecone, pgvector, Weaviate). When a user asks something, the system retrieves the most semantically similar chunks and passes them to the LLM as context. The LLM grounds its response in retrieved content — sharply reducing hallucinations and allowing source citations. RAG is preferred over fine-tuning when knowledge changes weekly, citation is required, or hallucination control matters.
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