How to Review AI-Generated Code - A 2026 Checklist

Why AI-generated code needs its own review discipline

By 2026, a large share of pull requests contain code that was generated, or heavily assisted, by tools like GitHub Copilot, Cursor, and Claude. That is not a problem in itself - these tools are genuinely productive. The problem is that AI-generated code fails differently from human-written code, and standard review habits do not catch those failures.

Human-written code usually signals its own uncertainty. A teammate leaves a // TODO, writes a hesitant commit message, or asks for a second opinion. AI-generated code does the opposite: it is uniformly fluent, well-formatted, and confident even when it is completely wrong. That fluency disarms reviewers. Studies of review behavior show people approve polished-looking code faster, and AI output is nothing if not polished. The takeaway: the confidence of AI-generated code is exactly why it needs more scrutiny, not less.

This guide is a practical checklist for reviewing and gating AI-generated PRs. For the broader mechanics of automated review, start with what AI code review is; here we focus on the human discipline of reviewing what the machine wrote.

The ground rule - if you submit it, you own it

Before any checklist, set the cultural rule that makes everything else work: the human who opens the pull request is accountable for every line in it, no matter who wrote it.

This closes the most dangerous gap in AI-assisted development, where the author did not fully understand the generated code, the reviewer assumed the author did, and the AI is not a person who can be accountable. Under that arrangement, nobody actually owns the change. The fix is a norm that authors must understand their AI-generated code well enough to explain and defend it in review - the same standard they would meet for code they wrote by hand. If an author cannot explain why a block is there, it does not merge.

The review checklist for AI-generated code

Work through these in order. The first items catch the most common and most damaging AI failure modes.

1. Verify the APIs and dependencies actually exist

Large language models hallucinate. They invent function names, method signatures, config options, and even whole packages that sound completely plausible but do not exist. The security angle is real - attackers have registered malicious packages under names that AI tools commonly hallucinate (“slopsquatting”).

• Confirm every imported package is one you intend to depend on and is not newly introduced without discussion.
• Check that called methods and parameters match the real library API, not a plausible-looking invention.
• Be suspicious of any new dependency the PR quietly adds to get something working.

2. Hunt for missing edge cases and error handling

AI reliably produces the happy path and reliably forgets the unhappy ones. Look specifically for:

• Null, undefined, and empty-collection handling.
• Error and exception paths - are failures caught, logged, and surfaced, or silently swallowed?
• Boundary conditions - zero, negative numbers, off-by-one, empty strings, timezone edges.
• Concurrency - shared state accessed without synchronization.

3. Scrutinize security harder than usual

AI-generated code weakens security in predictable ways: string-concatenated SQL, unescaped output, permissive CORS, disabled TLS verification, and hardcoded secrets that appeared “to make the example work.” Treat every security-relevant line as suspect. This is where an automated security layer earns its place, but a human should still confirm the change does not expand the attack surface.

4. Question the design, not just the correctness

AI optimizes for “make this snippet work,” not “fit this into our system.” It will happily reimplement something you already have, introduce a pattern your codebase has abandoned, or add a caching layer where the team standard is Redis. Ask: does this belong here, does it match our conventions, and is it the simplest thing that works? Unchecked, this is how AI assistance silently accrues technical debt.

5. Check that tests test the behavior, not the implementation

AI-generated tests frequently assert what the code does rather than what it should do - if the code has a bug, the generated test faithfully locks in the buggy behavior. Read tests for intent. A green test suite written by the same model that wrote the bug proves nothing.

6. Confirm the author can explain it

The final gate is human: have the author walk through anything non-obvious. If the answer is “Copilot generated it and it passed,” that is a request-changes, not an approval.

The takeaway: verify existence, then edge cases, then security, then design, then tests, then human understanding - in that order.

Where automated review fits

You should not do all of this by hand on every PR. An AI reviewer provides a fast, consistent first pass that clears the mechanical issues so your human reviewers can spend their attention on correctness, design, and ownership.

CodeRabbit runs 40-plus linters plus LLM analysis on every PR and posts inline comments within minutes, which is a strong catch-net for the missing-error-handling and style-drift categories above. Greptile indexes your entire codebase, so it is particularly good at the “does this fit our system” question - it can flag when AI-generated code breaks an assumption in a file the author never opened. Snyk covers the security layer specifically, scanning for the injection and secrets patterns AI code tends to introduce. And GitHub Copilot now offers its own review pass, useful if you are already standardized on it.

The important framing: automated review is a filter, not a substitute. Using one AI tool to review another AI tool’s output without a human who owns the result just moves the accountability gap around. For the strengths and limits of automated review against human review, see AI code review vs manual, and for picking a tool, our roundup of the best AI PR review tools.

Gating - how to enforce this without slowing everyone down

Turn the checklist into lightweight policy:

• Require the author’s understanding. Make “I understand and can explain this code” an explicit part of your PR template, not an assumption.
• Block on security and new dependencies. Gate merges on high-severity security findings and flag any newly added dependency for explicit human sign-off.
• Automate the first pass, keep humans on the last pass. Let an AI reviewer clear mechanical issues; never let it auto-merge without a human approval.
• Right-size the PR. AI makes it easy to generate huge diffs. Enforce small PRs so each one is actually reviewable - see best AI code editor comparison for how the generation tools differ here.

Conclusion

AI-generated code is productive and here to stay, but its fluency is a trap: it reads as trustworthy precisely when it should be questioned. Review it with a discipline aimed at its real failure modes - hallucinated APIs, missing edge cases, weakened security, off-pattern design, and hollow tests - and anchor the whole thing to one rule: whoever submits the code owns it. Use tools like CodeRabbit, Greptile, and Snyk to automate the first pass, and keep an accountable human on the last. That combination lets you capture the speed of AI generation without shipping code no one understands.

Further reading

• What Is AI Code Review? How It Works and Benefits
• AI Code Review vs Manual Review - What the Data Shows
• Best AI PR Review Tools in 2026
• AI Code Review for Security - Catching Vulnerabilities in the PR
• AI Code Review - glossary definition

Further Reading

• How to Review Pull Requests in VS Code (2026)