What is RAG?
Retrieval-augmented generation (RAG) is the pattern that lets a language model answer from your documents instead of its memory. At query time it retrieves the most relevant passages from a store you control, then generates an answer grounded in them — and, done properly, cites them. It’s the difference between a model that sounds authoritative and one that is.
Why RAG exists
A base language model is frozen at training time. It cannot see your claims policy, this quarter‘s RBI circular, or a patient’s record — and when asked about something outside its knowledge it tends to produce a fluent, plausible, wrong answer. RAG closes that gap by retrieving authoritative passages from a store you own and feeding them to the model as context for each request.
The result is that the model’s job shifts from recall to reasoning over supplied evidence. That is exactly the job you want it doing in a regulated workflow: not ‘what do you remember about prior authorization,’ but ‘given these three policy clauses, does this request meet the criteria?’
The three moving parts
The index
Your documents, chunked and embedded so they can be searched. Quality here — chunk size, metadata, freshness — sets the ceiling on everything downstream.
The retriever
The query-time search that decides which passages the model sees. Get this wrong and even a perfect model answers from the wrong source.
The generator
The model itself, prompted to answer only from the retrieved passages and to say so when they don’t contain the answer.
Where naive RAG breaks
Most first RAG builds use vector search alone: embed the query, find the nearest chunks by cosine similarity. This is great for paraphrase and terrible for precision. It misses exact-term matches — a specific drug name, an ICD code, a clause number — because those are lexical, not semantic. And it can‘t follow relationships: ’this policy covers this procedure for this plan’ is a graph traversal, not a similarity match.
For decisioning, that gap is the whole game. The fix is hybrid retrieval — combining vector similarity, full-text lexical search, and typed graph traversal so the retrieved passages are the ones a human reviewer would have pulled. That, in turn, is what makes citations trustworthy rather than decorative.
RAG FAQ
What does RAG stand for?
Retrieval-Augmented Generation. The model retrieves relevant passages from a document store at query time and generates its answer grounded in those passages, rather than relying on what it memorised during training.
Why use RAG instead of just asking the model?
A base model only knows its training data, can‘t see your private documents, and will confidently invent answers when it doesn’t know. RAG injects the specific, current, authoritative passages — your policy, your guidelines — so the answer is grounded in source you control and can cite.
Is RAG the same as fine-tuning?
No. Fine-tuning changes the model’s weights to shift its style or behaviour; it does not reliably teach new facts and is expensive to update. RAG leaves the model fixed and changes what you put in front of it at query time — so updating a policy is a document edit, not a retraining run.
Why isn’t vector RAG enough for regulated decisioning?
Pure vector search matches on semantic similarity, so it misses exact terms — drug names, policy codes, clause numbers — and can’t traverse relationships between entities. Regulated decisioning needs hybrid retrieval (vector + lexical + graph) so the passages retrieved are the ones a human reviewer would have pulled, and so each one can be cited verbatim.
Want to see this in your environment?
30-minute discovery call. We follow up with a draft SOW shortly after.
Talk to us about a pilot