Rethinking Data Model Design with Reasoning Models

This was one of those experiments where the gap between models became very obvious very quickly.

The setup was simple enough: design a schema for database connections, users, schemas, and permissions. GPT-4 produced a strong first pass, but the interesting part was not the first answer. It was what happened when the requirements became more nuanced.

When the same user needed to connect to the same database with different usernames and different schema access, GPT-3.5 missed the modeling problem even after being nudged. GPT-4 caught it and corrected the design.

The point was not that reasoning models remove the need for expertise. It was almost the opposite: they become much more useful when the person using them has enough context to notice gaps, challenge assumptions, and ask better follow-up questions.