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.