Vibe coding has dramatically reduced the cost of experimentation. Describe a product in natural language, receive screens and code, then improve it through conversation. That progress is real. Still, software that appears to work is not automatically ready to handle money, personal data, or a critical business process.
Where it works well
Landing pages, small internal tools, prototypes, UX tests, and low-risk automation are excellent uses. Within hours, a team can test whether a flow makes sense and whether users want the solution before investing in complete architecture.
Where the gap begins
The gap usually appears in the backend: user isolation, atomic transactions, failure handling, rate limits, backups, monitoring, and database migrations. An interface can look complete while exposing the wrong customer's data or creating duplicate records after a retry.
Security is not another prompt
Require a basic threat model, dependency scanning, secret management, authorization checks on every API, and tests against malicious input. AI-generated code needs review just like manually written code, sometimes more, because incorrect assumptions can still look convincing.
A practical route from demo to product
Freeze the core requirements, map sensitive data, select an architecture, review the code, add tests, and establish staging. Then release to a limited user group with monitoring and a rollback plan. The demo does not always need to be discarded, but it must become a system the team can explain and maintain.
The production test
Ask what happens when an external service fails, two users edit simultaneously, an employee leaves, a secret leaks, or a customer requests deletion. If the answer cannot be tested, you still have a demo rather than a production product.