How to Check Code AI Wrote When You Can't Read Code

You let an AI write some code, and now you’re staring at it with no idea whether it’s good, safe, or about to cause a disaster. Here’s the reassuring truth: you don’t need to read code to judge it. You need to judge its impact. A few plain-language questions catch the genuinely risky stuff, even if the code itself looks like hieroglyphics. This is the method careful people use, translated for someone who can’t (yet) read a line.

Stop trying to read it line by line

Reading code you don’t understand is slow and demoralising, and it’s not even the right approach. Experienced reviewers don’t give every line equal weight either: they look for what the code can affect. A typo in a harmless display function is no big deal. A mistake in code that deletes data is a catastrophe. So the skill isn’t reading; it’s spotting which parts matter. And that you can absolutely do.

The tool makers say as much themselves. GitHub’s documentation warns that Copilot Chat “may generate code that appears valid but is not semantically or syntactically correct,” and tells you to review and test it carefully, particularly for sensitive applications. Anthropic’s Claude Code docs put it in one line: you’re responsible for reviewing proposed code and commands for safety before you approve them. Nobody selling these tools promises that “it runs” means “it’s fine.”

Step 1: Make the AI explain itself

Your first and best tool is the AI that wrote the code. Ask it, in plain language:

• “Explain what this code does, simply, as if I don’t code.”
• “What could go wrong with it?”
• “Which passwords, keys or personal data does this touch, and where is each one stored?”
• “What did you install, and what is each thing for?”
• “What happens if someone enters something unexpected?”

A good AI assistant will answer honestly, and the act of asking forces the important details into the open. If the explanation is vague, changes when you ask again, or the AI admits it’s unsure, that’s your signal to slow down, not to trust it more.

One caution: the AI can be wrong about its own code, the same way it can hallucinate anything. So use its explanation as a guide, not gospel. That matters most for the high-impact areas below.

Step 2: Check the four high-risk questions

Whatever the AI says, run the code past these four questions. Any “yes” means pay attention here.

1. Does it handle secrets? Passwords, API keys, login tokens. Anthropic’s help centre compares an API key to a credit card number: if someone gets it, they run up charges on your behalf. It also says one of the most frequent causes of leaks is a key left in a public code repository. The app works exactly the same with the key hidden or with the key on display, so you won’t notice by testing.
2. Does it send data out, or leave it open? Two different failures. The code might send data to an outside service you didn’t know about. Or it might store data properly and leave the door open: Supabase, a popular database service for apps, warns that a table in an exposed schema without row level security “is readable and writable by any role with a grant on it.” You test logged in as yourself and only ever see your own data, so this never shows up.
3. Does it change or delete data? Anything that overwrites or removes information can do real, hard-to-undo damage if it’s wrong. The damage arrives the day something fails, not the day you test.
4. Does it run commands or install things on your computer? Code that executes system commands or pulls in packages deserves extra caution. OWASP, the open security foundation, lists AI models suggesting “insecure or non-existent code libraries” as a known risk, and describes attackers publishing malicious packages under the made-up names AI tends to suggest.

Code that does none of these is low-risk. Relax. Code that does any of them isn’t automatically bad, but it’s where mistakes hurt, so it earns a careful second look before you trust it.

Step 3: Check what you can with your own hands

You can verify more than you’d think without reading a line:

• Open the app in a private or incognito window, logged out, and try to see data you shouldn’t be able to see.
• Look up every package the AI listed by name. If you can’t find an official page for it, stop and ask the AI where it came from.
• Read each permission prompt before you approve it. If you don’t understand a command, ask the AI to explain it and what would happen if it went wrong. One thing to know about Claude Code: its docs say interactive terminal and VS Code sessions now start in auto mode, where a separate AI model reviews actions instead of you. If you want to see every prompt yourself, switch to Manual mode.

Step 4: Protect your secrets before you share anything

A specific, common trap: when you paste code or files to an AI for help (or to a second AI for a second opinion), you might unknowingly hand over passwords, keys, or personal data buried in them. Before you share, it’s worth checking what’s actually in there. Our free Paste Checker runs in your browser and flags API keys, lines like “password: …”, emails and phone numbers in what you’re about to paste, a simple guard against the most common beginner leak.

And if you think a key has already leaked, Anthropic’s advice is to revoke it immediately: delete it where you created it (for a Claude key, the API keys section of the Claude Console), then make a new one.

Step 5: Match your caution to the stakes

The right level of care depends entirely on what the code is for:

• A personal toy (a tracker only you use, with no keys that can cost you money): low stakes. Run it, learn from it, don’t stress.
• Anything with real users, data, or money: high stakes. Before this goes live, get someone who can read code to look, or at minimum slow down and make sure you understand the impact of every “yes” from Step 2.

This isn’t about fear; it’s about proportion. Move fast on the harmless stuff, and put the brakes on exactly where a mistake would matter.

The mindset that keeps you safe

You’ll hear “you own what ships” a lot, and it’s true even when you didn’t type a word of it. Owning it doesn’t mean reading every line: it means taking responsibility for the impact. Ask the AI to explain, check the four risk questions, verify what you can, guard your secrets, and get real help before anything real goes live. Do that, and you can build confidently with AI long before you can read code fluently.

Build with a setup that helps you review

Good habits are easier with a good setup. Our free AI for Coders tool helps you pick the right assistant, install it for your operating system, and get a starter setup that makes reviewing changes (seeing exactly what the AI did) part of your normal flow. Pair that with the impact-first method above, and “I can’t read the code” stops being a reason to feel unsafe.

Keep reading

GuidesWhat Is an AI Agent? (Plain English, No Jargon)A chatbot answers your questions; an AI agent goes and does things for you. The difference in plain words, with an example you've probably heard of.GuidesBest AI Coding Tool 2026: An Honest, No-Hype ComparisonThere's no single winner. Here's a fair look at Claude Code, Cursor, Devin Desktop, Copilot, Cline and Aider: what each is genuinely best at, and who it's for.GuidesWhat Is Vibe Coding? (And Is It Safe for Beginners)Building software by describing what you want in plain language and letting AI write the code. What it's genuinely good at, plus the honest catch.