How to review AI-generated code before you merge it

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Pre-commit checks for AI code

Before accepting AI-generated code, run a short pre-commit pass that checks more than formatting. The goal is to catch risky changes while the diff is still small.

• Review every changed file, not only the files mentioned in the AI chat.
• Run tests, type checks, linting, and a secret scan before commit.
• Check whether the AI changed authentication, authorization, CI, dependencies, environment variables, or deployment logic.
• Reject broad permission changes unless the diff explains why they are required.
• Make sure generated tests would fail for the original bug, not only pass the new implementation.

How do I validate AI suggestions before I accept them?

Validate AI suggestions by comparing the generated diff against the original requirement, the existing architecture, and production constraints. Do not accept a change because the explanation sounds confident. Accept it only after the code path, edge cases, and failure mode make sense.

1. Read the diff line by line. Look for extra files, unrelated refactors, or hidden configuration changes.
2. Trace input to output. For user input, files, webhooks, and API payloads, confirm validation happens at the boundary.
3. Check sensitive sinks. Pay attention to shell commands, SQL, file paths, redirects, logs, tokens, and deployment steps.
4. Run the smallest reproducible test. A generated fix should be exercised, not just compiled.
5. Ask what changed operationally. New dependencies, permissions, environment variables, and migrations all need a reason.

Is it safe to merge AI-generated code?

AI-generated code can be safe to merge after review, but it should not be merged on trust alone. Treat it like a contribution from a fast junior developer: useful, but not authoritative. The safest merge path is a small diff, clear tests, no secret exposure, no unnecessary permissions, and a rollback plan.

Use extra review for changes touching login, payments, production deployment, CI/CD, database migrations, file uploads, or anything that executes commands. These areas can fail even when the code looks clean.

Claude Code, Cursor, and Copilot security review workflow

The same review workflow works for Claude Code, Cursor, Copilot, and other coding agents:

• Scope check: did the agent modify only the requested area?
• Security check: did it add risky input handling, permissions, dependencies, or logs?
• Runtime check: did the exact changed path run locally or in CI?
• Release check: can this be reverted quickly if production behavior is wrong?

If the change is high-risk or business-critical, consider a separate human security review before merge.

AI-generated code merge review checklist

Before merging AI-generated code, review it against the original request and the real production constraints. The assistant may have solved the visible problem while introducing unrelated changes, weaker validation, broader permissions, or new dependencies.

• Confirm the changed files match the requested scope.
• Review all new dependencies and version changes.
• Run tests, type checks, linting, and secret scanning.
• Check user input, file paths, shell commands, SQL, redirects, and logs.
• Verify that generated tests fail for the old bug or risky behavior.
• Check deployment, CI, and environment-variable changes separately.
• Write down the rollback path before release.

When AI code needs deeper review

Use deeper review for authentication, authorization, billing, webhooks, admin functions, database migrations, production deployment, and anything that handles secrets or user data. These areas can look correct in a demo while failing under real users, malicious input, or operational edge cases.

Safe merge decision

It is usually safe to merge AI-generated code when the diff is small, tests cover the changed behavior, validation is explicit, no secrets are exposed, permissions are not broadened, and rollback is clear. If any of those conditions are missing, slow down and split the change into smaller reviewable parts.

Common mistakes

• Trusting the AI explanation instead of reading the diff.
• Accepting broad CORS or permission changes to make a test pass.
• Letting generated logs print tokens or request bodies.
• Merging dependency move hidden inside a feature change.
• Skipping runtime smoke tests because the code compiles.

Pre-commit workflow for AI code

A good pre-commit workflow should be boring and repeatable. Review the diff, run automated checks, scan for secrets, and test the changed behavior. If the AI modified configuration, deployment, or permissions, review that separately from the feature code.

When the diff is large, split it before merge. One pull request for generated tests, one for refactoring, and one for behavior change is easier to review than a single broad AI-generated patch.

Safe acceptance rule

Accept AI suggestions when you can explain the change without repeating the model’s wording. If you cannot explain why the code is safe, why validation is sufficient, or how rollback works, the change needs another pass.

• Small diff: easier to review and revert.
• Clear tests: prove the intended behavior.
• No secret exposure: scan before pushing.
• Narrow permissions: avoid convenience access.
• Runtime smoke: exercise the changed path.

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