Modeling Your Warehouse So AI Can Actually Use It

Everyone wants to point an LLM at their data — "ask questions in plain English," "let the agent pull the numbers." Then it confidently returns the wrong revenue figure, and trust evaporates. The problem usually isn't the model. It's the data layer underneath it. An LLM is only as good as the structure, definitions, and correctness of what it reads. Here's how to model for that.

If you let a text-to-SQL tool or an agent freelance joins against raw tables, it will invent a definition of "active customer" that doesn't match anyone else's. A semantic layer (or a set of certified metric models) fixes this: define each metric once — its grain, its filters, its joins — so the model composes from trusted building blocks instead of reconstructing business logic from column names. The semantic layer becomes the contract between your warehouse and anything, human or AI, that queries it.

Models reason over your schema the way a new analyst would — from names and docs. You reduce hallucinated joins dramatically just by making the warehouse legible:

Retrieval-augmented generation (RAG) lives or dies on what you chose as a chunk. That's a grain question, same as any fact table. Before you store vector columns next to your rows, decide: what is one "document"? A row? A paragraph? A record plus its context? Chunk too coarse and retrieval returns noise; too fine and it loses meaning. Keep rich metadata alongside each embedding (source, date, entity, permissions) so you can filter retrieval instead of hoping similarity alone is enough.

A dashboard with a bad number invites a double-take. An agent with a bad number states it as fact and moves on. So the fundamentals you already know carry extra weight:

In other words: the data-quality gates and reconciliation checks you build for humans become the guardrails that keep AI honest.

Related in In the Pipeline*: running those models safely once the data is ready.*