AI-Generated Code Review Checklist: What to Check Before You Ship

AI coding tools are good at producing plausible code. They are less reliable at preserving hidden requirements: the exact framework version, the shape of production data, the security model, the difference between an example and a real endpoint, or the timing of async side effects.

That means your review should start with practical failure modes. Do not stop at whether the code looks real. Ask what would break if you shipped it today.

1. 1. Does async work finish before the function returns?

   Look for async forEach, unreturned promises, fire-and-forget writes, background jobs without retries, and responses sent before side effects complete.

2. 2. Are auth and session checks enforced on the server?

   AI-generated UI checks are not enough. Confirm the API route, server action, webhook, or database write verifies the user and permission model.

3. 3. Are inputs validated before database, file, or network use?

   Check request bodies, query strings, uploaded filenames, JSON payloads, webhook data, and user-controlled IDs before they reach sensitive operations.

4. 4. Are secrets, tokens, and private data protected?

   Watch for hardcoded keys, verbose logs, client-side env usage, debug output, accidental response fields, and secret values included in error messages.

5. 5. Are file paths and uploads constrained?

   Generated code often handles files too casually. Confirm extension checks, size limits, path normalization, safe storage locations, and content-type handling.

6. 6. Are framework APIs current for your version?

   AI models can mix examples from old and new docs. Verify routing APIs, auth helpers, server/client boundaries, config names, and deprecated methods.

7. 7. Are dependencies real, necessary, and trusted?

   Check package names, install size, maintenance status, licensing, and whether the generated code invented an import that does not exist.

8. 8. Are errors handled without hiding failures?

   Empty catch blocks, generic fallback returns, and swallowed promise rejections make generated code look stable while hiding production failures.

9. 9. Do tests cover edge cases, not only the happy path?

   Generated tests often prove the example works. Add cases for empty input, invalid permissions, failed network calls, duplicate requests, and boundary values.

10. 10. Does a human understand the business logic?

    No scanner can know every product rule. Ask whether the code matches the actual user promise, pricing rule, refund rule, entitlement rule, or data policy.

This pattern is common in generated JavaScript and TypeScript. The function returns before the async work inside forEach is awaited.

async function notifyUsers(users) {
  users.forEach(async (user) => {
    await sendEmail(user.email)
  })

  return { ok: true }
}

A safer shape is to collect promises and await them explicitly.

async function notifyUsers(users) {
  await Promise.all(users.map((user) => sendEmail(user.email)))

  return { ok: true }
}

For more JavaScript and TypeScript examples, read the dedicated async code review guide.

Check AI Code is built for this first-pass review moment. Paste code or upload a file, then use the results as risk signals before you merge, deploy, or hand the code to a human reviewer.

• It can flag practical bug patterns such as async misuse and dangerous defaults.
• It can highlight security footguns around secrets, paths, commands, and input.
• It can help you notice generated code that depends on fragile assumptions.
• Pro users can use deeper review and Privacy Mode depending on the sensitivity of the code.

A clean scan is useful, but it is not a proof that code is safe. Check AI Code does not replace tests, type checks, manual review, dependency scanning, runtime monitoring, or a formal security audit.

Treat the output as a prioritized review assistant. It can help you spend attention faster, but your team still owns the final decision.

1. Ask the AI tool to explain the change and list its own assumptions.
2. Run the code through tests, type checks, and linters.
3. Paste the highest-risk file into Check AI Code for a first-pass scan.
4. Fix critical issues before asking for human review.
5. Have a human verify product logic, data policy, and user impact.