The cost to fix a flawed, AI-generated foundation is not static. It compounds monthly as new features are built on top of it, creating complex dependencies that must be untangled later. An audit at month two is a fraction of the cost of the same audit at month twelve.
Data from scans of thousands of live, 'vibe-coded' apps shows a high failure rate for security. Roughly 33% contain serious vulnerabilities like missing access controls or unvalidated webhooks. This risk should be assumed to exist in any unaudited AI-generated prototype until proven otherwise.
A demo of an AI-generated prototype answers, 'Does this look right?' This creates a false sense of completion. A real product must answer a harder question: 'Does this hold up with real users, data, and money?' The 'vibe' of a demo hides underlying architectural flaws that are invisible on the surface.
Instead of a generic checklist to harden an AI-generated prototype, audit it module by module. Assign one of three verdicts: 'Keep' for sound logic, 'Fix in place' for minor gaps, or 'Rebuild' for flawed foundations. This focuses resources on actual problems, avoiding costly work on parts that are already functional.