How to Review AI-Generated Code (Even If You're Not a Developer)

You can review AI-generated code without being a developer.

You won’t catch every security flaw or suddenly understand the whole backend. That’s fine. The goal is simpler: find the obvious risk signals before customers find them for you.

This AI-generated code review checklist is for founders who built with Cursor, Bolt, Lovable, v0, Replit, or any similar tool. It takes 30-45 minutes. You need your app, browser, and project files.

First, understand what you’re looking for

AI-generated apps usually fail in boring ways.

Not because the idea is bad. Not because the AI tool is useless. Usually the first version works for the exact demo path you asked for, then falls apart when someone does something slightly different.

A real user will:

• Submit a form twice
• Use a weak internet connection
• Paste weird text into a field
• Open the app on a phone
• Try a route they shouldn’t access
• Leave a tab open for six hours
• Click back during checkout

Your review is about asking: what happens when the app leaves the happy path?

Step 1: Run the app from a clean start

Start with the basics. If your project has a README.md, open it and follow the setup instructions exactly.

If it’s a typical JavaScript app, the commands probably look something like this:

npm install
npm run dev

Watch what happens.

Good signs:

• The setup instructions are clear
• The app starts without errors
• Environment variables are documented
• The terminal doesn’t fill with warnings

Bad signs:

• The README is missing or useless
• You need to guess which command starts the app
• The app only runs because your AI chat history remembers secret steps
• The terminal shows red errors before you even open the browser

This sounds basic, but it matters. If a fresh setup is fragile, future fixes will be slower and more expensive.

Step 2: Check for secrets in the wrong place

Open the project in your editor and search for these terms:

api_key
secret
OPENAI
STRIPE
SUPABASE
AWS
PRIVATE_KEY

You are looking for credentials that appear directly inside frontend files. In a React, Next.js, Astro, or Vite project, anything shipped to the browser can be seen by users.

Bad example:

const stripe = new Stripe('sk_live_abc123');

That sk_live key should never be in frontend code. The same goes for OpenAI keys, AWS keys, private Supabase service-role keys, email API keys, and database passwords.

Better pattern:

const stripe = new Stripe(process.env.STRIPE_SECRET_KEY);

Even then, check where the file runs. Server-only files can use private environment variables. Browser files cannot.

If you find real secrets committed in the repo, rotate them. Deleting the line is not enough. Git history may still contain the key.

Step 3: Open DevTools and use the app like a messy human

Open the app in Chrome. Right-click the page, click Inspect, then open the Console tab.

Now use the app normally. Sign up, log in, submit forms, open pages, change settings, upload files if the app supports it.

Then use it badly.

• Submit empty forms
• Enter an invalid email address
• Paste a 2,000-character message into a short field
• Click submit twice
• Refresh halfway through a flow
• Log out, then press the browser back button
• Try opening a dashboard URL while logged out

Watch the console.

Red errors are bugs until proven otherwise. Some are harmless, but don’t assume that. Copy them into a document with the page URL and what you were doing when they appeared.

Common errors worth taking seriously:

Cannot read properties of undefined
Failed to fetch
Uncaught (in promise)
Hydration failed
401 Unauthorized
403 Forbidden
CORS policy blocked

A console full of errors doesn’t always mean the product is doomed. It does mean the app is already telling you where it hurts.

Step 4: Test the forms like someone who doesn’t care about your rules

Forms are where many AI-built apps break.

Pick every form in the product and test:

• Empty submission
• Invalid email
• Very long text
• Special characters like <script> and emoji
• Duplicate submission by double-clicking
• Slow network mode in DevTools

A decent form should tell the user what went wrong and keep their input. A weak form loses the user’s message, crashes the page, or accepts nonsense.

Pay special attention to contact, payment, onboarding, and anything that writes to a database. A form bug can mean missing validation on the backend too.

Step 5: Check whether private pages are actually private

AI tools often make login screens that look right. The dangerous question is whether the backend agrees.

Try this:

1. Log in
2. Open a private page, such as /dashboard
3. Copy the URL
4. Log out
5. Paste the private URL into the browser

You should be redirected to login or shown a proper access-denied message.

Try opening API endpoints directly if you know them:

/api/users
/api/orders
/api/admin

You should not see private data while logged out.

This is not a complete security audit, but it catches a common AI-generated mistake: hiding data in the UI while leaving the API open.

Step 6: Run Lighthouse, but don’t worship the score

In Chrome DevTools, open the Lighthouse tab and run a mobile audit.

Look at four areas:

• Performance
• Accessibility
• Best Practices
• SEO

The score matters less than the warnings underneath it.

A performance score of 62 is not automatically a disaster. A warning that your landing page ships 6 MB of unused JavaScript is useful. A missing page title is easy to fix.

For SEO, check the basics: one clear page title, one meta description, proper heading order, indexable pages, descriptive link text, and no broken canonical URL. AI tools are decent at generating pretty sections. They are much less reliable at the boring metadata search engines need.

Step 7: Look at the file structure

You don’t need to understand every file. You’re checking whether the project looks organised or randomly accreted.

Good signs:

• Components, pages, and API code live in predictable places
• Repeated logic has been extracted into utilities
• Environment examples exist, such as .env.example

Bad signs:

• One file has thousands of lines
• The same function appears in five places
• Backend calls are scattered through random UI components
• Old experiments are still sitting in the app

Messy structure is not a moral failing. It happens fast with AI tools because generating new code feels cheaper than cleaning old code. But the mess becomes expensive when you need to fix bugs under pressure.

Step 8: Ask your AI tool to explain the risk before fixing anything

Most people ask AI coding tools: “Fix this error.”

Try asking better questions:

Review this code for production risks. Focus on security, error handling, data validation, and edge cases. Do not rewrite anything yet. Give me a prioritised list.

Then ask:

Which of these issues could expose user data, lose money, or stop signups?

The answers won’t be perfect, but the framing helps. You’re forcing the tool out of demo-building mode and into review mode.

Don’t blindly apply every suggested patch. AI can introduce new bugs while fixing old ones. Use the review to identify areas that need human attention.

Step 9: Create a simple risk list

At the end of your review, write down what you found under three headings.

Fix before launch: exposed API keys, private pages accessible while logged out, broken payment flow, forms that crash or lose data, and console errors on core journeys.

Fix soon: slow mobile pages, weak error messages, missing loading states, duplicate code in important flows, and no automated tests.

Watch later: minor layout issues, non-critical warnings, nice-to-have refactors, and content polish.

This stops everything feeling equally urgent. It also gives a developer a useful starting point if you bring one in.

What this review will not catch

A founder-level review is useful, but it has limits. You probably won’t catch database permission mistakes, subtle authentication bugs, race conditions under traffic, missing rate limits, broken webhook verification, poor database indexes, GDPR/data-retention problems, or logic bugs in payments.

Those need a proper code review. Sometimes they need a security review too. The point is to avoid flying blind.

The simple rule

If your AI-built app is a prototype, this checklist may be enough for now.

If it handles customer data, payments, private messages, health information, business workflows, or anything users depend on, get a second pair of eyes before launch.

AI tools are brilliant at getting you to a working demo. Production is a different job.

Want us to review your AI-generated code before users find the bugs? Get a free code assessment. We’ll check the visible app, the backend, and the risky edge cases this checklist can’t cover.