From Prototype to Production
Turning a working MVP into a dependable product: production checks, modular refactoring, data migration, audits, rewrite decisions and release gates.
Parts are listed in the recommended reading order.
25 checks before taking an AI-built project to production
A successful build is not production readiness. I review the system from source control and auth to monitoring and rollback.
Cleaning Preview Code: From Prototype Chaos to Modular Architecture
A controlled way to turn AI-generated prototype code into a production codebase without throwing away what already works.
From AI Prototype to Production Schema: Data Modeling and Migration Strategy
Turning loose prototype data into a relational production model with explicit ownership, constraints, backfills, cutovers and rollback rules.
What is an AI Product Audit, and what do I inspect?
Before adding more code to a fast-grown AI-built product, I map the product and system reality.
When should an MVP be rewritten, and when should it be rescued?
Rewrite is not a cleanliness decision. It is a product-risk, migration-cost and system-changeability decision.
Production Readiness Checklist for SaaS releases
A reusable release gate covering code, data, security, UX, operations and rollback.