AI
How to Ground AI Features in Your Own Data
Learn how RAG, retrieval, permissions and evaluation help ground AI features in company data so answers stay useful, traceable and relevant to real workflows.
TenzorDev Engineering · · 7 min read
The gap between an AI demo and an AI feature is not model quality. It is everything around the model: what it can see, what it is allowed to do, and how you know whether the answer was right.
Retrieval is the product
A language model with no access to your documents can only generalise. The moment a question depends on a policy, a contract or a record, the answer has to be assembled from retrieved context.
That makes chunking, indexing and ranking product decisions rather than infrastructure details. If retrieval returns the wrong three paragraphs, no amount of prompt tuning recovers the answer.
Permissions travel with the question
Retrieval must respect the same access rules as the rest of your system. The index should store the permission metadata alongside the content, and the query should filter before ranking, not after.
Evaluation before scale
Build a labelled set of real questions with expected answers. Score every change — prompt, model, chunk size, retriever — against it. Without this, quality becomes anecdote.