A tool that produces code can shorten the distance between an idea and something that runs. That is useful. It does not settle whether the implementation is appropriate for the system around it.
The important question for a client or engineering team is not how many lines were generated. It is whether someone understands the change well enough to accept responsibility for it.
Review the system, not just the snippet
A function can look reasonable in isolation and still be wrong for its context. It may duplicate an existing capability, bypass an authorization check, introduce an unsuitable dependency, or handle errors differently from the rest of the application.
Acceptance therefore needs more than a quick reading of plausible code. Review the requirements, the integration points, and the behavior under failure. Test the assumptions that matter to the business, not only the examples that made the first version work.
This applies whether the code was generated, copied from an approved reference, or written from scratch. AI changes the production process; it does not remove the need for a standard of acceptance.
Keep the change understandable
Smaller, well-scoped changes are easier to inspect and revise. Documentation should explain important decisions rather than restating the code. Dependencies should have a reason to exist. A future maintainer should be able to determine what the feature is supposed to do without recreating the original conversation with a tool.
Testing also needs judgment. A generated test that repeats the implementation's mistaken assumption can pass while proving very little. Use requirements and realistic edge cases as independent checks.
Be honest about the service
AI-assisted development is a description of how work may be supported. It is not the same as training a model or conducting machine-learning research. A business offering should explain the actual deliverable: a website, application, integration, or workflow that someone can use and maintain.
Speed matters, but only in relation to a useful result. The value of technical experience is deciding what to build, recognizing what is wrong, and making the finished system understandable enough to operate after the initial excitement has passed.