Shelf three · Language models & generative AI
Retrieval (RAG)
Kitchen table
Instead of answering from memory, the machine is allowed to look things up first, like an open-book exam. Ask about your school's rules and it does not guess. It goes to the shelf, finds the right page in the rulebook, reads it, and answers from what it just read, and it can show you the page. Much harder to make things up when the page is right there. The trick, of course, is finding the right page.
Whiteboard
Chunk the documents, embed them, retrieve by hybrid search (dense vectors plus keyword – dense alone misses part numbers and names), rerank, then generate with citations. Retrieval quality sets the ceiling: no prompt polish rescues a bad top-k, so evaluate retrieval separately from generation or you will tune the wrong half. The unfashionable determinants of success are document hygiene, chunk boundaries that respect structure, and freshness pipelines – librarianship, wearing a vector database.
Boardroom
This is how a general model answers from your policies, your contracts, your data, without the cost of training anything, and with a source you can check attached to every answer. That auditability is why it became the default enterprise pattern. When the answer is wrong you can see which document misled it, which is a governance property no raw model offers. The realistic budget note: the hard cost is not the AI, it is getting your document estate clean and current enough to be worth retrieving from.
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