The short version
Without RAG, a model answers from its trained knowledge and whatever you put in the prompt. With RAG, an application looks through a document collection first, adds the relevant material, and asks the model to answer from that evidence.
A workplace example
A new employee asks, “How do I expense a home-office monitor?” The assistant searches the current company policy, retrieves the paragraph about the equipment limit, and links back to it. If the policy is not available, it should say so rather than make up a limit.
Why teams use it
- Company information can change without retraining a model.
- Private documents can remain in the application’s controlled source set.
- Answers can point back to evidence.
- The system can test retrieval and answer quality separately.
The catch
RAG only helps if the right material is available, current, retrievable, and clear. It is a document-and-search system as much as it is a model feature.