How I evaluate AI-assisted code before merging

AI-assisted code should not get a lighter review because it arrived faster. It usually needs a more deliberate review because the change can look confident while hiding wrong assumptions.

Here is the checklist I use before merging AI-assisted work.

Does it solve the intended problem?

First, ignore the implementation and compare the result to the original goal. AI-assisted changes often solve a nearby problem instead of the actual one.

If the behavior is wrong, style and structure do not matter yet.

Are the boundaries right?

I check whether the change belongs where it was placed:

• component or service boundary
• data ownership
• API shape
• error handling location
• shared utility extraction

AI tools are good at filling files. They are less reliable at choosing the right boundary.

Are there tests for the contract?

The tests should describe the behavior that matters, not only increase coverage.

For AI-assisted work, I want tests around:

• edge cases
• failure paths
• permissions
• parsing
• data transformations
• regressions that would be easy to miss manually

What assumptions did the tool make?

AI-assisted changes often assume an API shape, a browser behavior, a data field, or a library feature. I look for those assumptions and verify them.

This is where many subtle bugs appear.

Is it maintainable by a human?

The final code should read like something the team would have written on purpose. If it is clever, overly generic, or inconsistent with the codebase, it needs simplifying.

The rule

AI can help produce the first draft. The engineer owns the merge.

That ownership is the difference between using AI as a tool and outsourcing judgment to it.