How AI Becomes Trustworthy
Part 2 of The Broker's Guide to AI — RAG, insurance-specific training, and the AI's working memory.
RAG: giving AI an open-book exam
RAG stands for “retrieval augmented generation.” Ignore the name — here's what it actually means: instead of letting the AI answer from memory, the system first finds the relevant documents (the actual carrier underwriting manual, the actual policy wording) and hands them to the AI before it answers. The AI then responds based on those documents, often pointing to exactly where the information came from.
Closed-book exam: the AI answers from memory and might guess. Open-book exam: the AI answers with the real manual open in front of it.
Insurance-trained AI: a specialist, not a generalist
A general AI knows a little about everything. An insurance-trained AI has been specifically taught the material that matters in your world: underwriting rules, coverage forms, and how brokers actually communicate with clients and carriers.
The difference is like hiring someone with a business degree versus someone who has worked the front desk at a brokerage for five years. Both are smart. Only one knows what “the carrier came back with a decline” means in practice.
Context windows: the AI's working memory
An AI can only hold so much information in mind at once during a conversation. That limit is called the context window. Picture a desk: you can spread out a certain number of pages, but eventually new pages push old ones off the desk.
This is why an AI might “forget” something from much earlier in a very long conversation, and why well-built tools feed it only the relevant documents instead of everything at once.
Sources and citations: trust but verify
The best insurance AI tools don't just answer — they show their work: “this answer comes from page 47 of the carrier's habitational manual, last updated March 2026.” That lets your team verify in seconds instead of taking the answer on faith.