Yes, AI can draft a database schema, constraints, indexes, and migrations from clear domain requirements. It should not be the final authority on data ownership, retention, authorization, or destructive changes. A useful result is a reviewed model plus repeatable migration evidence.
Describe each entity, identifier, relationship, lifecycle, required and optional fields, uniqueness rules, expected queries, and deletion behavior. Ask the agent to state assumptions before writing schema code. Require database-enforced constraints for invariants that must hold across applications. Review indexes against real access patterns rather than adding them mechanically. Generate forward and rollback migrations where the platform supports that pattern, and test them on realistic data volumes and edge cases. Verify concurrent writes, nulls, duplicate requests, and referential actions.
Separate schema design from data migration. A structurally valid change can still lose, misclassify, or expose existing records. Back up important data, rehearse the migration, compare counts and invariants, and monitor it after release. Privileged database credentials should remain outside prompts and repositories.
Start with a small domain and use AI to produce alternatives and tests, not just one confident design. Have an experienced owner approve critical models. When requirements, constraints, migrations, and recovery are explicit, AI can make schema work faster without turning the database into an undocumented source of risk.
