An AI coding assistant can produce a convincing patch before a developer has finished describing the task. That speed is useful—until the change touches more files than expected, quietly alters behavior, or is too tangled to review with confidence.
The key question is not whether the assistant can write code. It is whether the team can understand and safely decide what to do with the code it wrote.
Keep the task and the patch small
Give the tool a clear outcome and a bounded area of the codebase. Ask it to explain its assumptions before broad changes, and have it identify the files it expects to touch. Smaller tasks make it easier to spot when implementation has drifted from the request.
Keep generated work isolated from unrelated edits. A clean diff should show the change the assistant made without mixing it with a developer's in-progress work. If the tool cannot preserve that boundary, pause and resolve the workspace state before accepting more changes.
Review behavior, not confidence
Read the diff as if a teammate had submitted it. Check the control flow, error handling, input boundaries, and compatibility with existing patterns. Ask what changed beyond the intended behavior and whether those changes are necessary.
Run focused tests that exercise the requirement—not only a type check or a green build. For a user-facing change, verify the actual page or interaction. For non-trivial logic, add a regression test that would have caught the original problem.
Keep the human decision real
A summary from the assistant is useful navigation, not proof. The reviewer needs a real choice: accept, request a revision, or discard the patch. Make it easy to inspect generated commands and files, and never treat a generated commit or a passing test as automatic authorization to ship.
Teams can track review time, patch size, follow-up fixes, and reverted changes to learn where assistants help. The goal is not to approve more generated code; it is to spend less time on mechanical work while keeping engineering judgment intact.
Fast code generation is exciting. Fast, legible review is what makes it useful to a team.
