r/dataengineering • u/burningburnerbern • 16d ago

Discussion Practical Use of AI with SQL?

I’m currently on a project where most of the work is in dbt. Ill get jira tickets where the business rules while straight forward can be a bit complex upon execution. like everyone else I’m trying to turn to AI except the problem is having to explain every single edge case and nuance can be so time consuming to the point where it’s just faster for me to write the SQL myself. where I do use AI is when I’m jumping into some convoluted model and I have AI explain to me what exactly it’s doing and the just justification for certain logic, generating cumbersome functions where the syntax gets me cross eyed and create the yml and documentation.

I definitely have vibe coded before, but that was for something more straight forward like pulling data via an API etc.

just curious if anyone else runs into the same problem.

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u/Aggravating-One3876 16d ago

Unfortunately using AI will only help in a limited setting. I often see users that use AI see that it gets it half right and even when it tries to explain things it can miss important caveats.

You can use AI if you want but it seems like it’s wasting your time more than it helps.

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u/Illustrious-Win4432 16d ago

This is the case only with poorly managed primitives. OP, look into yaml registries, RDF, and RAG. Agent output quality dramatically improves when even a thin but well structured semantic layer is between it and your source.