What is RAG, and when do you actually need it?

Large language models are trained on public text. They do not automatically know your policies, tickets, or product manuals. Retrieval-augmented generation (RAG) fetches relevant passages from your own corpus and asks the model to answer using those passages.

You need RAG when answers must be current, citable, and limited to approved sources. You may not need it when the task is brainstorming, first drafts, or anything the model already knows well.

The hard parts are not the demo. Chunking, permissions, evaluation, and refresh pipelines decide whether the assistant is trusted. That is the focus of our Enterprise RAG program.