Database Architecture Mistakes AI Coding Tools Make (and How We Fix Them)
The recurring database design issues we see in AI-generated apps, and the fixes that matter most.
Database schemas generated through AI-assisted conversation tend to work for the exact use case they were designed and tested for — and develop cracks the moment real usage diverges from that. Here are the database issues we encounter most often, and why each one matters.
Most of these are fixable with migrations rather than a redesign, but the earlier they're addressed, the less data needs to be reshaped.
Missing Indexes on Frequently Queried Columns
Without an index, a database has to scan every row in a table to find matches — fine for a hundred rows, painfully slow for a hundred thousand. Foreign key columns, columns used in WHERE clauses, and columns used for sorting are the most common candidates that get missed.
No Foreign Key Constraints or Cascading Rules
Foreign keys aren't just documentation — they prevent orphaned records, like a comment pointing to a deleted post, and let the database enforce relationships automatically. Without them, that enforcement has to happen perfectly in application code, every time, forever.
Storing Everything in One Table ("God Tables")
It's common to see a single users or profiles table accumulate dozens of unrelated columns over the course of an AI conversation — subscription data, preferences, activity logs, all mixed together. This makes queries slower and access control harder to reason about.
Denormalization Without a Reason
Sometimes data is duplicated across tables for convenience without a caching or performance reason driving that choice. This leads to inconsistency — the same piece of information disagreeing with itself in different places — without the performance benefit that would justify it.
No Migration History
When schema changes happen through a UI or ad-hoc SQL rather than tracked migration files, there's no record of how the database got to its current state — making it hard to replicate the schema in a new environment or roll back a bad change.
Soft Deletes vs Hard Deletes — Picking One and Sticking to It
Some tables delete rows permanently; others add a deleted_at column and filter it out. Mixing both approaches inconsistently across a schema leads to bugs where "deleted" records still show up, or really-deleted records break foreign key references elsewhere.
Frequently Asked Questions
Do these issues mean the database needs to be rebuilt?
Almost never — these are typically additive fixes, like adding indexes, constraints, and migration tracking, rather than structural rewrites.
How do you find these issues without disrupting the live database?
By reviewing the schema and query patterns first, then applying fixes through migrations that can be tested against a copy of the data before running on production.
Is this relevant if my app is still small?
Yes — fixing these early, while the data volume is small, is far easier than fixing them after the issues start causing visible problems.
Related Reading
Not sure if your schema has these issues?
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