this post was submitted on 07 May 2025
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[–] morbidcactus@lemmy.ca 8 points 21 hours ago (1 children)

Gotta be real, LLMs for queries makes me uneasy. We're already in a place where data modeling isn't as common and people don't put indexes or relationships between tables (and some tools didn't really support those either), they might be alright at describing tables (Databricks has it baked in for better or worse for example, it's usually pretty good at a quick summary of what a table is for), throwing an LLM on that doesn't really inspire confidence.

If your data model is highly normalised, with fks everywhere, good naming and well documented, yeah totally I could see that helping, but if that's the case you already have good governance practices (which all ML tools benefit from AFAIK). Without that, I'm totally dreading the queries, people already are totally capable of generating stuff that gives DBAs a headache, simple cases yeah maybe, but complex queries idk I'm not sold.

Data understanding is part of the job anyhow, that's largely conceptual which maybe LLMs could work as an extension for, but I really wouldn't trust it to generate full on queries in most of the environments I've seen, data is overwhelmingly super messy and orgs don't love putting effort towards governance.

[–] jacksilver@lemmy.world 3 points 9 hours ago

I've done some work on natural language to SQL, both with older (like Bert) and current LLMs. It can do alright if there is a good schema and reasonable column names, but otherwise it can break down pretty quickly.

Thats before you get into the fact that SQL dialects are a really big issue for LLMs to begin with. They all looks so similar I've found it common for them to switch between them without warning.