Vibe coding: when is a prototype ready to become a product?
Generating an interface is easier. Check data, permissions, failures and maintenance before treating the prototype as a working product.
A dashboard appears on screen, buttons respond and a form accepts input. That may be enough to discuss an idea during a demonstration. In a product, you still need to discover what happens when two people edit the same record, the connection drops or someone tries to access data they should not see.
Here, vibe coding means directing much of an implementation through natural-language instructions, with an AI tool producing the code. The term describes a working method, not a level of quality. Someone might use it to explore a disposable interface or to build something that will subsequently be reviewed and maintained carefully.
A prototype should answer a question
Before generating screens, write down what you want to learn. “Does the customer understand how to book?” is a question a simulation can answer. “Can a booking ever be duplicated?” requires examining system behavior, including concurrency and persistence. The appearance of a page does not answer both questions at once.
Use invented data and identify what is simulated. If a button only changes a message on the page, describe it as an interaction demonstration. People can then assess the design without assuming that an integration, payment or storage operation already works. An honest demonstration usually produces more useful feedback about the work still needed.
Follow one record all the way through
Imagine a customer-registration prototype. Create a record, inspect the API response, reload the page and verify persistence. Open a second session and check that permissions still apply. Try editing a nonexistent record and see whether the interface provides an understandable message. These steps reveal whether the workflow exists beyond the browser’s visual state.
Then examine problematic input: empty fields, long text, duplicate values and repeated submission. Choose cases that make sense for the product. You do not need to invent hundreds of tests for an uncertain idea, but essential behavior needs a concrete demonstration before it handles real information. Keep a note of what has been exercised and what remains unknown.
Generated code still needs to be read
The GitHub Copilot responsible-use documentation recommends reviewing and testing suggestions, which may look valid without matching the developer’s intent.
Ask for an explanation of changed files and compare it with the code. Pay particular attention to where permissions are checked, how credentials are obtained and what happens when an operation fails. If nobody can explain why a change is necessary, the team is not ready to maintain it. Generating additional code to conceal uncertainty only creates more material that must eventually be understood.
Autonomy and publication are separate decisions
While exploring, work in a copy of the project with a history of changes. Access to external systems should have a clear purpose. The Cursor agent security guidance provides a reference for examining how approvals operate in that product.
Before real use, identify who will maintain the application, how a previous version can be restored and where errors will be observed. A prototype without these answers may still be valuable for testing an idea; it should simply not be presented as an operation that is ready to run. This distinction also protects the budget by making unfinished work visible.
The guide to evaluating models inside Cursor compares tools through completed tasks. The article on source-code privacy examines the context provided to them. The strongest evidence of progress is that one person can complete the intended workflow with correct data and permissions, while another can explain how it works.
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Tiago F SantiagoComments
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