Reading AI-Generated Code When You're Not a Developer

When an AI assistant writes code for you, it's tempting to hit "accept" and hope for the best. If you're not a developer, reading the code can feel pointless anyway. It's all strange symbols, right?

Here's the good news: you don't need to understand every line. You need to understand what changed and whether it matches what you asked for. That's a skill anyone can learn, and it's the difference between building with AI and just hoping AI builds the right thing.

Why reading AI-generated code matters

AI assistants are confident even when they're wrong. They'll occasionally change files you didn't mention, take shortcuts to make an error go away, or quietly leave a "TODO" where real logic should be. None of that is malicious. It's just what happens when a tool is trying very hard to give you a finished-looking answer.

You're the reviewer. You don't have to be a great one on day one. You just have to look.

Start with the diff, not the whole file

A diff is a view that shows only what changed: lines that were removed and lines that were added. Most AI coding tools show you one before you accept changes, and Git can show you one any time.

Even without knowing the language, you can read that diff like a sentence: the total used to be the number of items, and now it adds up each item's price. If you asked for "show the cart total in dollars", that looks right. If you asked to "fix the header color", it's a very strange change.

If you're working in a terminal, these three Git commands cover most of what you need:

git status        # which files changed
git diff --stat   # how much changed in each file
git diff          # the actual line-by-line changes

A simple way to review any change

Before accepting a change, I'd run through four quick questions:

1. Which files changed? Does the list make sense for what you asked? A header fix shouldn't touch your database code.
2. How big is it? A one-sentence request that produces hundreds of changed lines deserves a closer look.
3. Can you say what it does in plain English? If not, ask (see below).
4. Does it actually work? Reading isn't testing. Click through the feature after every change.

Make the AI explain itself

This is your superpower as a non-developer: you can ask as many "dumb" questions as you want, and the AI never gets tired of answering. Try prompts like these:

Explain this change in plain English, file by file. What does each part do?

Why did you change <file>? I only asked you to <request>.

What could this change break? What should I test?

Is there a simpler way to do this with fewer changes?

The last one is especially useful. AI assistants sometimes reach for a big solution when a small one would do, and simpler code is easier for both of you to fix later. The prompting guide has more on getting AI to explain its work.

Red flags worth stopping for

You don't need to catch everything. But if you see any of these in a diff, stop and ask before accepting:

• Changes to files you didn't ask about. Sometimes they're necessary. Ask why.
• Deleted or skipped tests. Tests are there to catch mistakes. Removing them to make things "pass" is a shortcut, not a fix.
• Error checks switched off. Lines like // @ts-ignore, eslint-disable or any often mean "I silenced the warning instead of fixing the problem".
• Errors that get swallowed. A catch block that does nothing means failures will happen silently, which makes bugs much harder to find later.
• Secrets in the code. API keys, passwords or tokens written directly in a file should live in environment variables (like a .env.local file) instead.
• New packages you didn't ask for. Every new dependency is more code you're trusting. Ask what it's for and whether you really need it.
• Fake data or placeholders. Hardcoded sample data or a TODO: implement this where real logic should be means it looks done but isn't.
• Anything that deletes data. Database changes with words like DROP or DELETE, or scripts that remove files, deserve a careful second read.

You'll understand more than you expect

The first few weeks, most of the code will look like noise. That's normal. But if you keep reading diffs and asking "what does this do?", patterns start to stick. You'll recognize a function, a loop, a database query. You'll notice when something looks off before you can say exactly why.

You won't become a senior developer by accident. But you'll become someone who can steer an AI confidently, which is what matters when you're building your own projects. I wrote more about that mindset in I'm not a developer — here's how I build apps with AI anyway.

— Spencer