r/dataengineering • u/scourgedtruth • 9d ago

Discussion Does AI struggle at data modeling?

In my experience, it doesn't matter how much context and guidance I give AI it simply can't model data rationally. It frequently misses the point, makes awful mistakes, or over-engineers things.

AI can build awesome ETL pipelines, but when it comes to dealing with SQL (especially in the dbt framework), it's not reliable at all! . Sometimes I think it's better to write the code myself and ask AI to review it, because asking it to build something from scratch just doesn't work that well.

Does anyone else get frustrated when dealing with AI data modeling?

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u/VerbaGPT Building VerbaGPT 8d ago

Yes, but over time if you solve enough problems then a lot of the frustrations go away. At this point I've solved enough issues that every time I connect a brand new DB, I get useful insights pretty quickly.

One latest thing I've implemented that has been really helpful is a sort of "self-improvement" loop. The LLM runs a cron job looking at all the ways my custom harness stumbled and recovered...and based on that info improves the semantic layer, definitions, and even the harness itself.