What Is RAG (Retrieval-Augmented Generation)?
By Karthick Selvam · Published
RAG combines a search step with a language model so answers are grounded in your own documents.
How it works
Documents are split into chunks, embedded and indexed. When a user asks a question, the most relevant chunks are retrieved and included in the prompt, and the model answers using them.
Why enterprises use it
It brings in current, private knowledge without retraining a model, and can cite sources so users can verify answers.
What makes RAG good
Clean source documents, sensible chunking, good retrieval (often hybrid keyword and vector search), re-ranking, and evaluation of both retrieval and answers.
Common failure modes
Retrieving the wrong passages, outdated documents, missing access controls and the model ignoring context.
Frequently asked questions
Does RAG eliminate hallucinations?
It reduces them but does not eliminate them; evaluation and citations remain important.
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