[cuuupid]

This is a big improvement, but I'm not a believer that SQL is the most appropriate query lang here. Personally am more bullish on language models being trained with ORMs, as those normally capture much more information about the fields.

e.g. Passing in some of my more complex table schemas related to flight data and asking about overflights, the model struggles to resolve out information related to aviation. However, GitHub Copilot writes me a perfect call to Prisma with the same single line instruction + information spanning the rest of my codebase.

[tillvz]

Agree that an approach that more semantically models the data is better, especially when you want to eventually let the non-technical users ask questions.

When you're on a higher abstraction level, it also allows you to make clear definitions (e.g. for certain KPIs) and define business logic that always needs to be applied to get the correct results.

There you don't want to leave it up to chance that a filter gets hallucinated in or out when you ask e.g. about your company's revenue.

At Veezoo (https://www.veezoo.com) we have taken the approach that instead of going directly to SQL. So when a user asks a question, Veezoo translates it first into a query against the Knowledge Graph (which represents the business objects, their relationship etc.). From there we compile it into a SQL query depending on the target database (they all have slight differences) without any AI involvement. In this compilation step we also make sure that the business logic is properly applied.

[thecalebf]

Neat, would you ever use a local model for that if it could work with ORMs?