How to Review AI-Generated Code Before It Reaches Production

AI coding tools have changed how fast code gets written. A feature that once took a day can now be drafted in minutes. But that speed creates a new bottleneck: review. If AI-generated code reaches production without a careful check, small mistakes become outages, security holes and angry customers. Knowing how to review AI-generated code before production is now a core skill for every developer, team lead and freelancer.

This guide gives you a practical review process, a printable checklist, and specific red flags that show up in AI-written code. It works for code from GitHub Copilot, Cursor, Claude Code, ChatGPT, Codex, Gemini or any other assistant.

Why Reviewing AI Code Is Different

Reviewing a colleague’s code and reviewing AI code are similar, but there are important differences:

• Volume: AI produces large changes quickly, sometimes touching many files at once. Big diffs are harder to review well.
• Confidence: AI code looks clean and comes with confident explanations, which makes reviewers relax too early.
• No memory of intent: the AI cannot explain business decisions or trade-offs the way a teammate can.
• Specific failure patterns: invented APIs, outdated patterns, missing edge cases and security shortcuts appear more often.
• Accountability: the person who merges the code owns it. “The AI wrote it” is not a defence when something breaks.

Step 1: Keep AI Changes Small

The best review starts before the code is written. Ask the AI for small, focused changes: one feature, one bug fix or one refactor at a time. A 50-line pull request gets a careful review. A 2,000-line pull request gets a skim.

If an AI agent produced a huge change, split it into smaller commits or pull requests before review. Most agents can do this for you if you ask.

Step 2: Read the Whole Diff, Not Just the Files You Expected

AI tools sometimes change files you did not ask them to touch: configuration files, lock files, tests, or unrelated code they decided to “clean up.” Always review the full diff in Git or your pull request tool. Look especially for:

• Changes to configuration, environment, build or deployment files.
• Deleted code or tests.
• New dependencies in package.json, requirements.txt, composer.json or similar.
• Changes to permissions, authentication or database schema.

Step 3: Confirm It Solves the Right Problem

Re-read the original task or ticket, then ask: does this code do exactly what was needed, no more and no less? AI sometimes solves a slightly different problem, adds features nobody asked for, or quietly changes behaviour elsewhere. If the change does more than requested, ask why, and remove what is not needed.

Step 4: Check Correctness and Edge Cases

Walk through the logic with real examples. Then think about what could go wrong:

• Empty, missing or null values.
• Very large or very small numbers, negative values, zero.
• Unexpected input types or formats.
• Time zones, dates and localization.
• Network failures, timeouts and partial failures.
• Concurrency: what if two users do this at the same time?

If tests do not cover these cases, add them. Our guide on how to test AI-generated code explains how.

Step 5: Verify Every API, Function and Package Is Real

One of the most common AI mistakes is calling functions or options that do not exist, or that existed in an older version. Check:

• Every external function call against the official documentation for the version you use.
• Every new package on the official registry, including exact spelling, maintainer and recent activity.
• That deprecated functions are not being introduced.

Fake or misspelled package names are a real security risk, because attackers publish malicious packages under names AI tools tend to invent.

Step 6: Do a Security Review

For any code that touches user input, authentication, payments, files or personal data, check the basics:

• Input is validated and sanitized on the server.
• Database queries are parameterized.
• Output is escaped.
• Permissions are checked for every sensitive action.
• No secrets, keys or passwords are in the code.
• Error messages do not leak internal details.

The OWASP Code Review Guide is a useful free reference. For a full list of common issues, read our article on security vulnerabilities in AI-written code.

Step 7: Check Readability and Maintainability

Code that works today still has to be understood and changed later. Ask:

• Does it follow your project’s style, naming and structure?
• Is it more complex than it needs to be? AI sometimes over-engineers simple tasks.
• Does it duplicate existing helpers or utilities in your codebase?
• Are comments accurate, or do they describe what the AI intended rather than what the code does?
• Would a new team member understand it?

Store your coding conventions in a file the AI can read, such as project rules in Cursor, Copilot instructions or a CLAUDE.md file. That reduces style problems at the source. See our guide to the best setup for AI coding agents.

Step 8: Check Performance

• Database queries inside loops (the N+1 problem).
• Loading large data sets into memory when only a few rows are needed.
• Missing indexes for new queries.
• Repeated API calls that could be cached.
• Heavy work happening on every page load.

Test with realistic data volumes, not just the three test records in your development database.

Step 9: Run Automated Checks

Automated tools catch many issues quickly and consistently:

• Unit, integration and end-to-end tests.
• Linters and formatters.
• Type checking.
• Security scanners (Semgrep, CodeQL, Snyk) and dependency audits.
• Secret scanning.

Run them in CI on every pull request, and do not merge until they pass.

Step 10: Test on Staging Before Production

Deploy to a staging environment that mirrors production. Click through key user journeys, check logs for errors, and confirm that background jobs, emails and integrations work. For WordPress sites in particular, never let an AI agent edit a live site directly. Our article on the risks of AI coding agents on live WordPress sites explains why.

Step 11: Plan the Rollout and Rollback

• Deploy at a time when someone can watch for problems.
• Use feature flags for risky changes so you can turn them off instantly.
• Make sure you can roll back quickly, and have a fresh backup before database changes.
• Monitor error logs and performance after release.

Red Flags in AI-Generated Code

How Teams Should Handle AI Pull Requests

If several people on your team use AI tools, agree on a few simple rules so reviews stay consistent:

• Label AI-heavy pull requests. A simple tag like “AI-assisted” reminds reviewers to look for the common AI failure patterns.
• The author must understand the code. The developer opening the pull request should be able to explain every line. If they cannot, it is not ready for review.
• Include the prompt or task summary. A short note on what the AI was asked to do helps reviewers judge whether the result matches the intent.
• Two reviewers for risky areas. Changes to authentication, payments, data deletion or infrastructure get a second human reviewer.
• Track problems. If AI-generated code causes a bug in production, note what kind of mistake it was. Over time you will learn which tasks need extra care.

A Real-World Example

Imagine you ask an AI agent to “add a CSV export button to the orders page.” It returns a working button. A quick test shows the file downloads correctly. But a careful review finds four problems:

1. The export runs a separate database query for every order, which will time out on large stores.
2. It does not check user permissions, so any logged-in user could download every customer’s details.
3. Customer names with commas break the CSV columns because values are not properly escaped.
4. It added a new CSV library, even though your framework already includes CSV support.

None of these show up in a quick “does it work?” check. All of them would cause real problems in production, and one is a serious data leak. This is exactly why a structured review matters.

Use AI to Help Review AI

AI can also be a helpful reviewer. Ask a second model, or the same model in a fresh conversation, to review the diff:

“Review this diff as a senior engineer. List bugs, missing edge cases, security issues, performance problems and anything that does not match the task description. Be specific and cite line numbers. Do not rewrite the code.”

Tools like GitHub Copilot code review and other AI review bots can comment on pull requests automatically. Treat their comments as suggestions, not final judgment. A human reviewer still decides.

Printable Review Checklist

1. The change is small and focused.
2. I read the full diff, including unexpected files.
3. It solves exactly the requested problem.
4. Edge cases are handled and tested.
5. All APIs and packages are real and current.
6. Security basics are covered.
7. Code matches our style and is easy to understand.
8. Performance is acceptable with realistic data.
9. All automated checks pass.
10. Tested on staging.
11. Rollback plan is ready.

For Freelancers and Agencies

When you deliver AI-assisted code to clients, your review process is part of what they pay for. Document it, keep it consistent, and be transparent about how you use AI. Our guides on billing clients for AI-assisted code and adding an AI disclosure clause to your contracts can help.

Frequently Asked Questions

Should all AI-generated code be reviewed?

Yes. Every change that reaches production should be reviewed by a human, regardless of who or what wrote it.

How long should reviewing AI code take?

It depends on the size and risk of the change. Small changes may take a few minutes. Changes touching authentication, payments or data deserve a much deeper review.

Can AI code review tools replace human reviewers?

Not yet. AI reviewers catch many issues, but they miss context, business logic and subtle design problems. Use them to support human reviewers.

What is the biggest risk with AI-generated code?

Over-trust. Code that looks clean and is explained confidently can still contain bugs or security holes. A consistent review process is the best protection.

Final Thoughts

AI writes code fast, but you are responsible for what ships. Keep changes small, read every diff, verify APIs and packages, check security and edge cases, run automated tests, and use staging before production. Build these steps into your routine and AI becomes a reliable teammate instead of a risky shortcut.