Retrieval-augmented generation (RAG) is a technique where an AI system retrieves relevant source documents first, then generates its answer grounded in what it retrieved — instead of answering from the model's training data alone.
Why it matters in an agency
An LLM asked "what are the general liability limits on the Henderson account?" from training data alone will invent an answer. The same model, given the actual policy documents retrieved from the agency's files, will read the limits off the page and cite where it found them.
The difference is between a system that sounds authoritative and one that is.
How it works in practice
- Agency documents — policy forms, carrier guidelines, SOPs, past correspondence — are indexed
- An incoming question is used to retrieve the most relevant passages
- Those passages are supplied to the model alongside the question
- The model answers using them, ideally citing the source
The insurance-specific advantages
- Currency. Carrier appetite, forms, and guidelines change constantly. Retrieval reads today's document; a trained model reflects whenever training ended.
- Auditability. A cited answer can be verified. This matters a great deal when the answer concerns coverage.
- Containment. Agency data stays in the retrieval layer rather than being baked into a model.
The honest limitation
RAG reduces fabrication; it does not eliminate it. If retrieval surfaces the wrong document, the model will confidently ground its answer in the wrong document. Retrieval quality is the whole game, and it degrades quietly when document organization is poor — which returns the problem, as it so often does in agency automation, to data hygiene.