Pairing a model with a search step that retrieves relevant documents into its context before it answers.
Broad technical consensus on what this term means. Disagreements that invoke it are about the world, not the word.
A model's weights are frozen at training time and its context window is finite, so it cannot know yesterday's news or a company's private files. RAG bolts on a retrieval step: the question is used to fetch relevant documents, typically by embedding similarity, and those documents are placed into the context window before the model answers.
It is the dominant enterprise pattern for grounding models in current or proprietary knowledge, cheaper and faster to update than fine-tuning. It narrows hallucination by giving the model the right material, but does not eliminate it, the model can still misread or override what it retrieved.