The short answer
AI-assisted code still needs an agreed behaviour and an independent check. As a client, you should be able to understand what changed, which existing features were protected and how the team verified the public result. The production method does not replace acceptance.
Generated code still needs a definition of done
A developer adds a filter quickly with AI assistance. In the demonstration, selecting a category works. On your phone, the long category names overlap; with a keyboard, the control is hard to use; and an empty search gives no explanation.
The happy path was real, but it was not the whole feature. The question for a client is less about how the code was typed and more about what standard the finished work has met.
Describe the feature as a customer would use it
A brief for a catalogue filter should explain which items can be selected, how search combines with the filter and what happens when nothing matches. It should also say which working features must remain intact.
These decisions should be clear before the team asks an AI tool to produce code. Otherwise, a convincing implementation can reflect assumptions nobody intended to approve. A short, specific behaviour description is easier for both the developer and the client to review.
A worked example
Suppose a business requests a searchable article library while keeping every existing article address accessible.
Where it goes wrong
The first generated version filters a sample list, but hides the rest of the archive behind a failed request and changes the old links.
What to do instead
Define the full catalogue, existing routes, combined search behaviour and empty state in advance. Review those requirements on the built and published website.
The acceptance is about the requested feature and its protected boundaries, not about whether the generated code looks plausible.
Ask for checks that go beyond the demonstration
Useful checks include the relevant phone layout, keyboard use, long or missing content and a customer opening the page directly. A developer may also need automated tests for logic and integration. The right combination depends on what the change could affect.
The review should challenge the proposed solution rather than simply repeat its assumptions. A test that confirms a button exists is not the same as checking that a customer can complete the intended task. Keep evidence tied to the behaviour being accepted.
Make responsibility clear after the code is generated
Someone remains responsible for reviewing the change, understanding its dependencies and delivering a recoverable release. AI assistance does not remove the need to protect client data, credentials or unrelated website functions.
After publication, verify the affected public journey. If it fails, the team should explain the issue and correct or roll back the result. Faster production is useful when it reaches a dependable outcome, rather than creating a larger queue of confident but unverified changes.
Take this into your next conversation
Questions to ask your agency
- What behaviour are we accepting?
- Which existing features must keep working?
- Which failure and edge cases were checked?
- Was the published journey verified separately?
Common questions
Is AI-assisted development automatically less reliable?
The use of a tool alone does not establish quality. Look at the specification, review, checks and responsibility for the delivered result.
Should clients review the code themselves?
Clients should be able to review the intended behaviour and relevant evidence. Technical code review remains a professional responsibility, with scope matched to the change.
From explanation to implementation
This guide is published by NAVIFLARE, a website, CRM and AI workflow practice. Source notes identify the platform guidance; worked examples explain a method rather than claim a client result.
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