RAG: giving AI access to a document library

A model is not a current document archive

Language models can explain concepts, but they do not automatically know a company's internal procedures or the latest revisions of its files. Retrieval-Augmented Generation, or RAG, tackles part of that problem by pairing generation with a search system.

The basic flow is straightforward: index documents, retrieve material relevant to a question, and provide the retrieved passages to a language model. The assistant can then attempt an answer grounded in available text.

Retrieval is the hard part

If search returns the wrong document, even a fluent answer can be wrong. Document quality, chunking, version control, access permissions, and search ranking all matter.

Sources should remain distinguishable from generated answers. Clickable citations help readers inspect the original passage. When evidence is missing, the system should express uncertainty rather than invent a reference.

Not a guarantee against hallucinations

RAG does not eliminate mistakes or prompt injection. Retrieved documents are external inputs and must be treated as data rather than commands. When files are confidential, retrieval must respect each user's permissions.

A good RAG system is more than a language model connected to a vector database. It is a maintained, tested, and auditable document pipeline.