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What is RAG? Retrieval-Augmented Generation Explained

RAG retrieves relevant documents and injects them into LLM prompts, reducing hallucinations and enabling domain-specific AI. Learn how production RAG systems work.

Retrieval-Augmented Generation (RAG) is an AI architecture pattern that enhances Large Language Model responses by retrieving relevant information from a knowledge base before generating an answer.

Instead of relying solely on the model's training data (which can be outdated or incomplete), RAG injects real, current, domain-specific documents into the prompt. This dramatically reduces hallucinations and enables AI applications that answer questions about YOUR data — company docs, product manuals, legal contracts, medical records.

How RAG Works

  1. Index: Documents are chunked, embedded into vectors, and stored in a vector database
  2. Retrieve: When a user asks a question, the query is embedded and the most similar document chunks are retrieved
  3. Augment: Retrieved chunks are injected into the LLM prompt as context
  4. Generate: The LLM generates an answer grounded in the retrieved documents

Learn RAG Engineering — Free

Our free Production-Grade RAG Systems Engineering course teaches you to build scalable, reliable RAG systems — not toy demos. 16 modules, 31 labs.