1) Lock down data access first
Before scaling features, verify data access boundaries. Ensure authenticated users can only access their allowed records and operations. Prototype defaults are often too open for production risk tolerance.
2) Separate environments clearly
Use distinct development, staging, and production configurations. Avoid sharing secrets or databases across environments. Production issues become much easier to diagnose when environments are predictable.
3) Make deploys repeatable
If deploy success depends on memory or ad-hoc manual steps, it will eventually fail at the worst time. Build a repeatable release path and define rollback instructions before launch.
4) Add observability on day one
Set up error tracking, request logging, and uptime alerting before user growth. You want visibility before incidents, not after customer complaints.
5) Test edge cases, not just happy paths
AI-generated code often demos well but misses edge-case behavior. Focus testing on bad input, retries, timeouts, race conditions, and permission boundaries.
6) Ship with an operator checklist
Create a one-page runbook: deploy command/path, rollback steps, key dashboards, and emergency contact points. This cuts response time dramatically when something breaks.
Need help implementing this quickly?
Quiver Ridge runs short prototype-to-production sprints for Lovable, Replit, Bolt/v0, and custom AI-built stacks.