Vibe coding got you a demo. What gets you to launch?

What is vibe coding?

Vibe coding describes an approach in which a person uses natural-language instructions and AI-generated changes to create software, often by repeatedly asking for adjustments until the application appears to work. It can help turn an idea into something visible quickly. The engineering question is what sits behind that visible result.

For a business approaching launch, the useful distinction is between a convincing demonstration and an application whose critical behaviour has been understood and checked. That applies whatever tools were used to write the code.

Why an AI-built app can fail after a good demo

The interface gets ahead of the implementation.

A screen can display a feature that relies on placeholder data, incomplete backend logic or an integration that has never been connected. Follow a real transaction from the first input to the stored result. Identify where the product actually performs the work and where it only appears to.

Individual fixes change shared behaviour.

A generated change may resolve the example in front of you while affecting another part of the application. If each repair is judged only by the latest screenshot, the team can lose track of previously working journeys. Keep a short set of critical end-to-end checks and rerun them after relevant changes.

Development conditions hide production gaps.

A single developer account, a local database and successful dependencies do not exercise every condition customers will encounter. Review permissions between users, failure handling and the production configuration before treating local success as launch readiness.

A real example of the gap

In our US banking software rescue, AI-generated scaffolding had left substantial work unresolved. More than half the backend code was missing, the server was crashing and serious security failures had compromised data. The Conso4s team recovered the build and launched it in six weeks.

The case demonstrates the importance of checking the implementation behind a product. It does not establish that all AI-generated applications have those faults. Read the banking rescue.

What to do when prompting no longer moves the project forward

1. Record the failure. Note the steps, expected result and observed result. Keep secrets and customer data out of shared examples.
2. Identify the essential journey. Choose the action the product must complete, rather than starting with every visible defect.
3. Trace the implementation. Establish the relevant code, data and dependencies, including unfinished work.
4. Define a check for the repair. Include the original failure and neighbouring behaviour that must continue to work.
5. Scope the next decision. Decide what to retain, repair or investigate before committing to a wholesale rewrite.

Use AI-generated code review as a discipline.

GitHub’s guide to reviewing AI-generated code emphasises human oversight and verification. A useful review considers what the change is meant to accomplish and how the implementation behaves in the application.

Our production-readiness checklist covers completeness, data handling, failures, environment and ownership. For an independent investigation, see code audit and review services.

When to bring in a recovery team

Ask for help when important behaviour remains unexplained, the same failures return after changes or your team cannot establish what is ready for launch. A free initial assessment can help define the problem. Detailed review and implementation are separately scoped through our vibe coding rescue service.