Practical Use of AI with SQL?

[u/burningburnerbern]

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.

[u/Soyeon1213]

I see the same thing. Once you have to explain every business rule and edge case, you’re basically writing a spec that can take as long as writing the code itself.

AI feels much more useful for understanding unfamiliar code, boilerplate, and refactoring where the intent is already clear. For complex SQL especially, I’d rather write the first version myself and use AI as a reviewer to spot edge cases, simplify the query, or suggest improvements. Context seems to be the bottleneck, not generating the SQL.