RAG is the most widely used pattern for grounding AI in accurate, current, and organization-specific information. When a query comes in, the system retrieves the most relevant documents or data chunks from a knowledge base, includes those chunks in the prompt to the language model, and the model generates a response based on the retrieved content.
An employee asking a policy bot about the parental leave process and getting an answer based on the HR policy document updated last month — rather than a generic approximation — is experiencing RAG working as intended. Without RAG, AI answers are constrained to what the model learned during training, which is often outdated, generic, or missing proprietary organizational knowledge entirely.
