Database Issues in AI-Generated Apps

What goes wrong with the database?

AI generates a database schema that looks correct at first glance. You have tables, columns, data gets saved. The problem is that the schema is flat. Customer name, email, and phone are stored directly on every order instead of being linked to a Users table. With 10 test records, nobody notices. With real traffic, things start breaking.

Data duplication is everywhere. Changing a customer's email means updating hundreds of rows instead of one. These are classic update anomalies — a problem solved in database engineering decades ago, but AI doesn't know about it because it generates code that "works," not code that's correct.

On top of that, there are no indexes. A query that should take 50ms takes 3 seconds. The database scans the entire table row by row because nobody told the AI to add an index on the column you're filtering by.

How to fix it

1. Schema audit. We review the entire database and identify flat structures, missing relations, and duplicated data. We design a normalized schema that eliminates update anomalies.
2. Add indexes. We analyze queries and add indexes on columns used in WHERE, JOIN, and ORDER BY clauses. A query that took 3 seconds starts running in 50ms.
3. Fix N+1 queries. We replace loops that run individual queries with efficient JOINs or batch queries. 101 queries become one.
4. Connection pooling. We configure a connection pool so the database doesn't drown under new connections on every request.
5. Database engine migration. If the current database doesn't fit the use case (e.g., SQLite in production), we migrate to PostgreSQL or MySQL — engines designed to handle concurrent traffic.