How to Review AI-Generated Code: A Checklist for Beginners

You ask your AI assistant to add pagination to an endpoint. Six seconds later it hands you a 40-line diff that looks clean, compiles, and even includes comments. Your cursor hovers over "Accept."

That moment is the most important skill test in modern programming. Anyone can generate code now. The skill that separates people who ship reliable software from people who ship mystery bugs is what happens between "the AI answered" and "I merged it."

This guide gives you a concrete, repeatable checklist for reviewing AI-generated code, written for developers who are early in their careers and using tools like Copilot, Cursor, or Claude Code every day.

The checklist at a glance

1. Read before you run. Never execute or commit code you have not read.
2. Read the shape first. Inputs, outputs, and structure before line-by-line logic.
3. Compare the diff to what you actually asked for. Hunt for spec drift.
4. Hunt for scope creep. Flag every change you did not request.
5. Check the classic bug spots. Boundaries, empty inputs, error paths, and falsy values.
6. Check fit. New dependencies, style mismatches, and duplicated helpers.
7. Make it explain itself, then verify. Ask why a line exists, then run and test it.

The rest of this article walks through each step with real examples. If you want the extended version, with graded exercises where you review actual AI diffs, this maps directly to the code review module of our AI-Assisted Development course.

Why reviewing AI code is different from reviewing human code

When a colleague sends you code, you can make useful assumptions: they know the project, they had a reason for every change, and if something looks odd you can ask what they meant.

AI-generated code breaks all three assumptions. The model does not know the parts of your codebase it never saw. It produces plausible-looking code even when it is guessing. And it will confidently justify a wrong answer if you ask it casually, because it generates explanations the same way it generates code: by producing likely text, not by consulting an inner understanding. We cover the mechanics of that in Why AI coding tools hallucinate APIs.

So the reviewing posture flips. With a trusted colleague you look for oversights. With an AI you verify from scratch, the way you would review code from a fast, talented contractor you hired yesterday: probably fine, but you check the work before it goes in the wall.

One more reason this matters for beginners specifically: whoever accepts the code owns it. When the bug surfaces in three weeks, "the AI wrote that part" is not an explanation anyone accepts. Reviewing is how you keep the authorship you are accountable for anyway.

Step 1: Read before you run

The single most protective habit is also the simplest: the code does not run, and definitely does not merge, until you have read it.

This sounds obvious and is constantly skipped, because AI output arrives with a finished, confident texture that human work-in-progress does not have. Clean formatting and tidy comments feel like evidence of correctness. They are not. The model formats guesses exactly as neatly as it formats facts.

Set the rule now, while your habits are still forming: generated code is a draft submitted for your review, not an answer.

Step 2: Read the shape first, logic second

Do not start at line one and read downward. First pass, squint at the structure:

• What are the inputs and outputs? Do the types and names make sense?
• What are the moving parts: loops, branches, early returns, external calls?
• Does anything exist that surprises you: an import you do not recognize, a second function you did not ask for, a global variable?

The shape pass takes thirty seconds and catches a surprising share of problems, because AI mistakes are often structural: a helper that duplicates something your project already has, an unnecessary async wrapper, a whole error-handling layer bolted on uninvited.

Only after the shape makes sense do you read the logic line by line. Two passes are faster than one careful pass, because the shape pass tells you where to slow down.

Step 3: Compare the diff to what you asked for

AI tools drift from specifications in small, quiet ways. You ask for one behavior, and the code implements something adjacent. This is called spec drift, and it is easy to miss because the code is not wrong in general. It is wrong about your requirement.

Say your requirement is "users 18 and older can register" and the generated check is:

if (user.age > 18) {
  allowRegistration(user);
}

That code rejects every 18-year-old. The spec said 18 or older, which is >=. Nothing about the line looks broken. It only fails against the requirement, which means you can only catch it by re-reading the requirement while you review, not by asking "does this look like reasonable code?"

Practical technique: before reading the diff, restate your request in one sentence. Then check the code against that sentence, clause by clause. Boundaries ("at least", "no more than", "up to"), plurals ("all matching items" versus the first one), and defaults are where drift hides.

Step 4: Hunt for scope creep

Ask an AI to change one thing and it will often change five. Renamed variables, reformatted sections, a swapped library, a "while I was here" refactor. Suppose you asked only for a limit parameter and the diff also contains:

- const resp = await axios.get(url);
- return resp.data.items;
+ const response = await fetch(url);
+ const data = await response.json();
+ return data.items.slice(0, limit);

The slice is what you asked for. The switch from axios to fetch is not, and it silently changes error behavior: axios rejects on HTTP error status codes, while fetch resolves and hands you a failed response to check yourself. That difference is exactly the kind of thing that turns into a production incident with no obvious cause.

Unrequested changes are not free improvements. Each one is an unreviewed change riding along inside a diff you were told was about something else. Your move as reviewer is simple: for every hunk in the diff, ask "did I ask for this?" If not, either review it as seriously as the main change or tell the tool to remove it and keep the diff minimal.

Step 5: Check the classic bug spots

AI-generated code fails in patterns. Once you know the patterns, review gets dramatically faster because you know where to aim. The short version of the list:

• Boundaries. Off-by-one errors, > versus >=, first and last elements.
• Empty and zero inputs. Empty arrays, empty strings, zero, and the difference between "no value" and "falsy value."
• Error paths. The happy path is usually right. What happens when the network call fails, the file is missing, or the input is malformed?
• Duplicates and ordering. Does the logic still hold when two items are equal, or when input arrives unsorted?

For a worked tour of these failure patterns with runnable examples, see The 7 classic bugs in AI-written code. Reviewing with a mental "zero, one, many, weird" checklist catches most of them: what happens with zero items, one item, many items, and a weird input like a negative number or a string of spaces?

Step 6: Check fit, not just correctness

Code can be correct in isolation and still wrong for your project.

• New dependencies. Did the diff quietly import a package you do not use? Every new dependency is a maintenance and security decision, and it should be yours, not the model's. Occasionally AI tools invent package names entirely, which attackers exploit by registering the invented names. Treat any unfamiliar import as unverified until you look it up.
• House style. If your codebase uses async/await and the AI writes promise chains, or your project has a shared formatDate helper and the AI writes a new one inline, the code adds friction even though it works.
• Existing patterns. The model cannot see conventions that live in files it was never shown. It is your job to know that "we already have a validation layer for this."

A useful prompt addition for next time: tell the tool the conventions up front. But at review time, fit is on you.

Step 7: Make it explain itself, then verify

Two closing moves before you accept anything.

First, interrogate the diff. Pick the line you understand least and ask the tool: "Why is this line here? What breaks if I delete it?" Two things can happen, and both help you. Either you get a concrete explanation that teaches you something, or the model retracts and rewrites the code, which tells you the line was decoration. One warning: phrase it neutrally. If you ask "isn't this wrong?", models tend to agree with your framing and "fix" code that was actually fine. Ask what the line does, not whether it is bad.

Second, climb the verification ladder as far as the stakes require:

1. Read it (you have, if you got this far).
2. Run it on a normal input and watch it behave.
3. Test it on the edges: zero, one, many, weird. Write the edge cases down as real tests if the code is staying.
4. Prove it against the requirement: does each clause of the original request have a check covering it?

Low-stakes script? Read and run may be enough. Code that touches money, auth, or user data? You climb the whole ladder. The stakes decide, not your patience.

A 60-second version for daily use

Once the full checklist becomes habit, the daily version compresses well:

Shape. Spec. Scope. Edges. Fit. Explain. Verify.

Seven words, one pass each. On a small diff that is genuinely about a minute, and it is the difference between using AI tools with confidence and accumulating a codebase you are afraid of.

Frequently asked questions

Should beginners even use AI to write code?

Yes, with the review habit attached from day one. The risk for beginners is not using AI, it is accepting AI output without reading it, which quietly outsources the learning along with the typing. If you review every diff with the checklist above, AI-generated code becomes a stream of worked examples in your own codebase, which is a genuinely good way to learn. If you skip review, you ship code no one on the team understands, including you.

How long should reviewing AI code take?

As a rule of thumb, reviewing should take a meaningful fraction of the time writing it yourself would have taken. If generating took ten seconds and reviewing took five, you did not review, you skimmed. For a typical small feature diff, a few focused minutes is normal, and it is still a massive net speedup over writing from scratch.

Can I ask the AI to review its own code?

You can, and it sometimes catches real issues, but it cannot be your only reviewer. The model that made a wrong assumption tends to keep making it while reviewing. A better pattern: ask it to explain the code line by line (explanations expose gaps), ask a fresh session to critique it (a fresh session does not defend earlier reasoning, though it may introduce fresh mistakes of its own), and keep the final read for human eyes. Yours.

What should I do when I do not understand the generated code at all?

Do not accept it. Ask the tool to simplify it, explain it, or solve the problem in a more basic way, and keep asking until you could defend every line to a teammate. "It works but I cannot explain it" is exactly the code that becomes unmaintainable three months later. Understanding is the acceptance bar.

Keep going

Reviewing is one of the three durable skills of AI-era development, alongside directing (writing specs and prompts that produce good code in the first place) and verifying (proving the result actually works). Our AI-Assisted Development course trains all three across 61 interactive lessons, including a capstone where you review a real AI agent's diffs with planted defects and have to catch them before "merging." The first three lessons are free, no account required to preview.