What Is RAG in AI?

RAG stands for retrieval augmented generation. The simple idea is to retrieve relevant information first, place it into the model's context, and then ask the model to generate an answer from that grounded material.

A typical RAG system ingests documents, chunks them, creates embeddings, stores them in a vector store, searches for relevant chunks, and passes the selected context to the model with the user's question.

RAG is useful when knowledge changes often, when answers need citations, or when a business wants an AI assistant to use private documents without retraining a model.

RAG does not automatically guarantee truth. Bad chunking, stale documents, weak ranking, or missing source metadata can still produce confident but incomplete answers.