How to Check AI-Written Code: A Beginner’s Review Checklist
Ten passes that catch most AI code problems before they cost you: read it, run it, break it on purpose, check the imports, and never ship a secret.
The assistant wrote it in four seconds. Your job — the job that makes you a programmer rather than a spectator — is the next ten minutes: checking it. Professionals are blunt about why this step exists. In Stack Overflow’s 2025 Developer Survey, more developers distrust AI tools’ accuracy (46%) than trust it (33%), the top frustration is code that is “almost right, but not quite” (66%), and 45% find debugging AI-generated code more time-consuming than their own. Checking is not paranoia; it is the workflow. GitHub builds it into the product’s definition: with Copilot in assistive mode, “you review and apply each suggestion” — review is step one, not an optional extra. This checklist turns that instruction into ten concrete passes you can run on any AI-written code, in order, in about ten minutes. It belongs to our pillar series on AI for coding beginners; the mistakes guide shows what happens when these passes are skipped.
Highlights
- Pass order matters: understand it → verify the pieces exist → run it → stress it → secure it → then keep it.
- The three questions that catch most bugs: Can I explain every line? Does every function it calls actually exist? What happens on bad input?
- Use the AI against itself: asking the assistant “what could be wrong with this code?” is a legitimate review pass — as long as you verify its answers too.
- Done means tested: code that has not run with normal AND edge-case inputs is not checked, however carefully you read it.
The checklist
1. Read the whole thing before running anything
Read top to bottom without executing. You are looking for the shape: what goes in, what comes out, what are the main steps. If the structure itself surprises you — an approach you didn’t expect, a library you didn’t ask for — pause there. Surprises are where misunderstandings between you and the model live.
2. Explain every block in your own words
For each function or block, say out loud (or write in a comment) what it does and why. Any block you cannot explain is a block you do not own yet. Ask the assistant to explain that specific part — “explain lines 30–45 as if teaching a beginner” — then re-explain it yourself. If your two explanations disagree, resolve the difference before moving on; one of you is wrong and it matters which.
3. Verify every external call against the official docs
List every function, method or library feature the code uses that you didn’t write. Check each one exists, with that name and those parameters, in the official documentation. This pass catches the most characteristic AI failure: the plausible-but-invented API. Models generate the call that should exist, and beginners waste hours debugging code whose only bug is being fictional. Two minutes in the docs beats two hours in the debugger.
4. Run it and test the normal case
Execute the code with the most ordinary input you can think of and confirm the output is exactly right — not roughly, exactly. Wrong-but-close output is the signature AI failure (the survey’s 66% “almost right” frustration), and it slips past a casual glance. If there are tests, run them; if there are none, your expected outputs, written down before you run, are the test.
5. Test the edges
Now feed it the inputs that break things: empty text, zero, a negative number, the largest value, a missing file, no internet, a name with an apostrophe. Beginners test the path where everything works; bugs live on the paths where it doesn’t. Note which edge cases fail — an assistant asked to fix a specific, observed failure (“entering an empty name crashes with this error”) produces far better fixes than one asked to “make it robust.”
6. Follow the data
Pick one piece of input and trace it through the program by hand: where is it stored, how is it changed, where is it used. Check the loop counts (off-by-one errors are the most common logic bug in generated code), the comparison operators, and any place a value changes type. This sounds slow; on beginner-sized programs it takes three minutes and catches what reading alone misses.
7. Scan for security red flags
You do not need to be a security expert to spot the classics: passwords or API keys written directly in the code; user input pasted into a database query or a web page without cleaning; code that runs whatever text it is given (eval and friends); downloads or deletions that happen without asking. Generated code inherits the average habits of its training data, including the bad ones. If any red flag appears, ask the assistant specifically how to remove it — and check the replacement with this same checklist.
8. Check the failure behaviour
What happens when things go wrong mid-program — the file doesn’t exist, the network drops, the user types letters where numbers belong? Does it crash with a wall of errors, fail silently, or handle it and explain? AI code typically handles the happy path and ignores failure entirely. For learning projects a crash is a lesson; for anything others will use, unhandled failure is the bug that matters most.
9. Ask the AI to attack its own code — then check its answer
A genuinely useful trick: paste the code back and ask, “You are a sceptical senior developer reviewing this. List everything that could be wrong with it.” Assistants are often surprisingly good critics of code in front of them, including their own. Treat the list as leads, not verdicts: verify each claimed problem is real (passes 1–8) before fixing it, and notice what it missed. You are training the exact judgement METR’s study found missing even in experts, who believed AI had sped them up by 20% while it had slowed them by 19% — the belief and the measurement only agree if you check.
10. Clean it, name it, save it
Remove leftover debug prints and dead code, rename anything misleading, add one comment at the top saying what the program does, and only then save it as yours — in version control if you use it. The standard for “yours” is the standard this whole checklist builds toward: you could explain it, defend its choices, and find its bugs without the assistant open. If you can’t, it isn’t finished, however well it runs.
What this means
A checklist like this looks like beginner scaffolding, but it is better understood as the actual profession, written down. Code review — reading other people’s code sceptically, testing its edges, checking its assumptions — is what senior engineers are paid for, and AI has simply moved that activity from something done occasionally to something done on every single file, including your own. The survey numbers make the direction of travel plain: usage of these tools is near-universal (84% of developers use or plan to use them), sentiment is cooling (favourable views down to 60% from over 70%), and trust is explicitly conditional — the profession’s settled position is “generate freely, verify always.” Beginners who learn checking as a first-class skill are not being slowed down by caution; they are skipping the industry’s most expensive lesson, the one where unverified code meets real users. There is a US-workplace dimension too: employers increasingly hand juniors AI tools on day one and judge them on the quality of what they ship, not on how much they generate. Ten minutes of structured scepticism per file is the cheapest professional advantage available to a new programmer right now.
Adopting the checklist early also changes your relationship with the tools in a way beginners rarely anticipate: it makes you faster, not slower, within a few weeks. Developers in GitHub’s research conserved mental effort on repetitive work precisely because they trusted their process for the parts that mattered; a reviewer with a routine can accept a good suggestion in seconds and spot a bad one at the same speed, while a reviewer without one oscillates between blind trust and blanket suspicion. In US classrooms and bootcamps the same pattern is now formalised — AI use is increasingly permitted with a verification log attached. Treat this checklist as that log’s paper form: ten passes, a few minutes, and the code is defensibly yours.
Bottom line
Checking is not scaffolding around the profession; it is the profession. Ten sceptical minutes per file is what turns four-second code into software you may put your name on.
Read Next
- How to Use an AI Coding Assistant: A Beginner’s First Session, Step by Step
- 7 Mistakes Beginners Make With AI Coding Tools (and What to Do Instead)
Sources
- About GitHub Copilot — GitHub Docs (accessed 7 November 2026)
- AI section, 2025 Developer Survey — Stack Overflow (accessed 7 November 2026)
- Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity — METR (accessed 7 November 2026)
