A working demo is only the first answer
A generated sign-up form may look complete before anyone checks whether it stores a submission or handles an interrupted request. That is the gap between a prototype and a product people depend on. Vibe coding makes the first draft fast; the useful next step is to inspect what each apparent feature actually does and which decisions the prompt left open.
The phrase is associated with letting an AI generate code while the person steers mainly through natural-language requests and observed behavior. It is not a precise engineering standard. Simon Willison, writing as a developer who experiments with these tools, draws a useful distinction between AI-assisted programming, where the developer still inspects and understands the code, and a looser style of accepting output on feel. That distinction matters most as the project grows.
Why it is so good for an early prototype
A prototype needs to answer questions quickly. Will people understand the layout? Does the workflow make sense? Can a form collect the right information? An assistant can set up pages, styles, and small interactions faster than someone starting every file by hand. You can describe a change, run the app, notice what is wrong, and ask for another pass.
That loop lowers the cost of trying an idea. A non-developer can make a concrete draft instead of discussing screens only in words. A developer can spend less time on boilerplate and more time on the hard behavior. In both cases, the human still needs to decide what the app is supposed to do and whether its behavior answers the real question.
The first trap is visual success. A signup form can look finished while it never stores a submission. A dashboard can show attractive numbers that are placeholders. A payment screen can simulate a purchase without handling the edge cases that matter when money moves. Each apparent feature needs a test against a real requirement.
The code inherits every unasked question
Ask an assistant to “add login” and it must choose some design. Should accounts be created by invitation? What happens after too many failed attempts? How is a password reset handled? What personal data is stored? If the request does not say, the generated implementation may choose defaults that look reasonable in a demo and are wrong for the product.
The same problem appears in ordinary features. A calendar must handle time zones. A file upload must limit types and size. A search page needs to distinguish no results from a failed request. An app used by other people needs accessible controls, useful error messages, and a way to repair mistakes. These are product decisions as well as coding tasks.
Repeated prompts can also make a codebase harder to reason about. One request adds a new state variable; another adds a similar one elsewhere. A quick patch fixes the visible case but breaks a different screen. If nobody inspects the structure, the cost of each next change rises. A demo can survive that for a while. A product with users cannot rely on luck indefinitely.
What a responsible build loop looks like
Start with a narrow goal and a clear example of success. For a booking form, specify the fields, validation, time zone behavior, and what a confirmation means. Let the assistant implement a small step. Run the app and test both the expected path and one failure: missing input, duplicate action, lost connection, or an expired session.
Then inspect what changed. A developer can review the code directly. Someone with less coding experience can still ask for a plain explanation of where data goes, what is stored, and what happens on an error. If the answer is vague, the implementation deserves closer review before deployment. Independent tests and expert review become more important when the app handles payments, sensitive information, or irreversible changes.
Keep the distinction between a mockup and a live product visible. A button that simulates sending a message should say so in the development version. A database table built from fake entries should not quietly become a source of truth. Clear boundaries prevent a successful prototype from being mistaken for a production system.
Test the path a demo usually skips
A generated prototype may show a polished sign-up form with one happy-path account. Before using it for real customers, try the cases that a screenshot never reveals: duplicate email addresses, interrupted network requests, long names, missing permissions, expired sessions, and a user who needs to delete an account. Check what the server accepts independently of what the browser form permits. A visible input limit is not a security rule if the server trusts any value sent to it. OWASP’s input-validation guidance explains why security checks must run on the server even when the browser already checks the form.
Dependencies and generated code also need ownership. Who will update a vulnerable package? Where do errors appear when a payment or email fails? Can someone explain how data is stored and backed up? An AI assistant can help inspect these questions, write focused tests, and propose fixes, but accepting its next confident patch without understanding the failure can compound the problem. A responsible loop uses the quick draft as a map of what to examine, then checks the actual behavior with realistic inputs before calling it a product.
The useful middle ground
AI-generated code is neither automatically disposable nor automatically ready. It can be a strong drafting tool when someone owns the requirements, tests the behavior, and understands the important decisions. “Vibe coding” is most useful as a way to explore an idea quickly. The moment real people rely on the result, the work shifts toward maintainability, security, accessibility, and support. Those responsibilities do not disappear because the first version arrived in one prompt.
