A base model knows the public internet up to a date. It does not know your contracts, your policies, or your product. RAG is how you close that gap without retraining anything.
The problem RAG solves
Ask a general-purpose model about your business and it will answer fluently and often wrongly, inventing plausible details it has no way to know. In any setting where accuracy matters, that is unusable.
Retrieval-augmented generation fixes this by giving the model your documents at answer time, so it responds from what it actually found rather than what it vaguely remembers.
How a RAG system works
First, your sources are parsed, split into passages, and embedded into a vector index. When a question comes in, the system retrieves the most relevant passages and hands them to the model along with the question.
The model is then constrained to answer from those passages, and to cite them, so every answer is traceable back to a source.
Why naive RAG disappoints
Stuffing a few documents into a prompt is not RAG, and it shows: the wrong passages retrieved, context lost across pages, confident answers with nothing grounding them.
Real retrieval is an engineering problem (chunking strategy, hybrid search, re-ranking, and evaluation against actual queries) and that is where quality is won or lost.
When to reach for RAG
RAG fits anywhere people need fast, cited answers over a body of text: contracts, filings, policies, support history, internal wikis, product documentation.
If your users are searching, skimming, and copy-pasting to answer questions, a well-built RAG system can collapse that into a single grounded answer.