Vibe coding is a way to build software by describing changes to an AI coding tool and accepting much of the generated output with limited manual review. It can help explore an idea or make a small, reversible change. Risk grows when the same fast loop is used for work with many dependencies, security consequences, or long-term owners.
When the fast loop works well
This approach fits tasks where feedback is quick and mistakes are cheap to reverse. A throwaway prototype, a local script for a known input, or a small visual adjustment can often be checked by running it and inspecting the result. The developer still owns the outcome: generated code should be read, exercised, and revised before it becomes part of a maintained system.
- Keep the change small. Give the tool one outcome to implement and name the files or interface it should touch.
- Set a check before prompting. Write down what observable result will show that the change works.
- Review the patch. Look for side effects, missing error paths, and assumptions the prompt did not cover.
Where the risk grows
A plausible patch can still miss a requirement. A green test can still check the wrong behavior. A long chat can contain decisions that are absent from the code review and issue record. These gaps become harder to see as several prompts build on earlier generated changes. Ask which expectations were checked independently before the change was accepted.
For example, a requested settings change might include a read-only page and an authorization check. An agent can update the visible page and pass a component test while leaving the API route available to a role that should be denied. The patch looks reasonable and the test is green, but one acceptance criterion was never checked across both paths. Listing the allowed roles and testing a denied request can expose the gap. Choose checks from the requirement and include the boundary cases that would make a wrong result visible.
A practical verification loop
Before implementation, record the requested behavior and acceptance criteria in the work item. Ask the coding tool for a focused change and a list of edited files. Run checks that exercise the expected behavior, inspect the diff, and have a person review choices that require product or domain judgment. Attach the commit and check references so another engineer can trace what happened.
For the product workflow, see solutions. Review the current security boundaries before connecting a tool.
When to add more control
Add review and automated checks when a change affects shared interfaces, customer data, access control, migrations, or production behavior. Keep the checks proportional to the consequence of failure. AI-assisted development can remain fast when each task has a clear boundary, a durable record of intent, and a check that tests the behavior people rely on.