Guide

AI Code Review Checklist

A practical checklist for reviewing AI-assisted code changes with scoped diffs, tests, security checks, and evidence before final merge approval.

AI-assisted code review starts with the request, not the model’s explanation. A confident summary can make a patch feel safer than it is. The reviewer should inspect whether the diff solves the requested behavior, stays scoped, and produces evidence that the project trusts.

The first check is whether the diff solves the requested behavior without unrelated changes. If the patch rewrites surrounding code, changes naming schemes, modifies dependencies, or adjusts configuration outside the task, pause the review. Unrelated churn makes defects harder to see.

Use this checklist with How to Verify AI-Generated Code for merge evidence and AI Code Review Workflow for team-level review routing.

It is intentionally practical rather than exhaustive: each item should either change the merge decision or name a risk that still needs evidence from the repository.

Check the request and acceptance criteria

Restate the requested behavior in one or two sentences. What should now happen? What should not change? What user, API, job, parser, or workflow is affected?

Then compare that statement to the diff. A model may solve a nearby problem, add a broader feature, or introduce an abstraction that was not requested. If the acceptance criteria are unclear, ask for clarification or write a narrow test before reviewing style.

Do not accept “the code is improved” as a review goal. Improvement needs a measurable contract: passing test, fixed bug, reduced error, safer input handling, or clearer behavior.

Inspect the diff for scope

Review the changed files before reading the explanation. Look for:

• Files unrelated to the task.
• Broad formatting churn.
• Dependency or build changes.
• Deleted tests or weakened assertions.
• New global state.
• Changed defaults.
• Silent behavior changes in shared helpers.

Small diffs are not automatically safe, but broad diffs need stronger evidence. If the model changed more than requested, split the patch or reject the extra changes.

Verify tests and failure paths

A good AI-assisted patch should be connected to tests or a clear manual reproduction note. Use AI-Generated Code Testing when deciding which test layer fits.

Check for one happy path and at least one meaningful failure path. For input handling, include malformed and missing values. For permissions, include a denied user. For data writes, include duplicate or partial failure. For UI changes, include empty, loading, and error states when relevant.

Generated tests need review too. They may assert the model’s implementation instead of the actual behavior. Ask whether the test would fail against the original bug. If not, it may not be verification.

Check security and data boundaries

Focus on the boundary touched by the patch:

• User input: validation, escaping, parsing, and hostile cases.
• Auth: role checks, default-deny behavior, and privilege boundaries.
• Data writes: idempotency, rollback, audit trail, and partial failure.
• Secrets: no tokens in code, logs, tests, prompts, fixtures, or screenshots.
• External calls: timeout, retry, error handling, and rate limits.

If any boundary is unclear, require a reviewer note before merge. Do not let the model’s assurance replace inspection.

Record the review decision

Use a simple decision record:

• Accepted: evidence proves the requested behavior.
• Accepted with risk: evidence is partial and the risk is named.
• Needs changes: defect, missing test, or scope issue.
• Rejected: cannot be verified or introduces unsafe behavior.

AI-assisted code should be rejected when it cannot be verified with project-owned evidence. This includes patches that hide failing checks, remove tests, invent APIs, change unrelated modules, or require trust in the model instead of trust in the repository.

Verification checklist

Before merge, confirm:

• The requested behavior is clear.
• The diff is scoped.
• Existing checks ran or the gap is documented.
• New tests cover behavior, not just implementation.
• At least one failure path is covered for risky changes.
• Security, permissions, data writes, and secrets were checked when relevant.
• The final decision names evidence and residual risk.

For repeatable review prompts, pair this page with AI Code Review Prompts and the downloadable Code Review Checklist.

FAQ

What should be checked first in an AI-assisted code review?

The first check is whether the diff solves the requested behavior without unrelated changes.

When should AI-assisted code be rejected?

AI-assisted code should be rejected when it cannot be verified with project-owned evidence.