TLDR:
Retrieval-Augmented Generation (RAG) is a technique that combines LLMs with information retrieval systems—typically vector databases—to produce outputs grounded in specific documents or data sources, reducing hallucinations and enabling use of private or up-to-date information.
How RAG Works
A RAG pipeline has three stages. First, source documents are chunked, embedded into vector representations, and stored in a vector database. Second, at query time, the user’s question is also embedded and used to retrieve the most semantically similar chunks from the database. Third, the retrieved chunks are inserted into the LLM’s prompt as context, and the model generates an answer grounded in those chunks. Modern RAG systems add re-ranking, query rewriting, and multi-hop retrieval for complex questions.
Why RAG Beats Pure LLMs
Pure LLMs have three limitations RAG addresses: knowledge cutoffs (LLMs only know what was in their training data), inability to access private data, and hallucinations (confident-sounding but false outputs). RAG grounds outputs in retrievable documents, making the system updatable without retraining, enabling private data access without exposing it during training, and providing citation trails for verification.
Enterprise RAG Patterns
Common enterprise RAG applications include: customer-facing knowledge bases (Intercom Fin, Zendesk AI), internal knowledge assistants (Glean, Notion AI), legal research assistants (Harvey, Hebbia), and document Q&A products. Building production RAG requires careful chunking strategy, embedding model selection, vector database choice (Pinecone, Weaviate, Qdrant, pgvector), and evaluation infrastructure to measure retrieval quality and answer faithfulness.