Coding agents like Claude struggle to get meaningful information from databases. Even though they're good at writing correct SQL, they fall short where it matters - fetching the right answers. When asked a complex question, they consistently fumble their way through the schema catalogs and table descriptions, and then make best-guesses about how to join them, hoping to find some data that looks reasonable.

The reason for this is simple - they don't know your domain. It's like hiring an expert in database syntax and expecting them to know how your company works. The solution is equally simple - teach the agent what the data means. Give them a guide to how your database is laid out, how its joined, what column names mean and what kind of queries make sense. All the folk knowledge that that expert hire would eventually acquire in their first 6 months.

The technique for teaching agents the meaning of a schema is called a semantic model, there's an open standard that's easy to stick to, and the results are pretty terrific. A single file can take an agent from burning tokens to hallucinate an answer, to one-shotting the correct results.

In this talk we'll go through the details of semantic models and the standard, why it's worth using the standard rather than rolling your own, and techniques for creating effective semantic models quickly. All in the service of a simple outcome - making a scalable database analyst that's effective from day one.