Can You Ship AI-Written Code As Is? A 10-Point Verification Checklist for Beginners

Thanks to AI, people without a programming background can now build software themselves. That is a genuinely good change. But there is one thing you must remember:

AI is very good at producing code that looks right. Looking right and being right are not the same.

This article lists ten things to check before you use AI-generated code for real, written so that you can follow along even if you are not a developer.

1. Did you actually run it?

The most basic step is also the one most often skipped. The AI saying "this will work" does not mean it does.

• Run the code yourself and see the expected result with your own eyes.
• For a website, open it in a real browser and click every button.
• Check it on a phone too. Things that look fine on a desktop often break on mobile.

2. Did you try entering the wrong things on purpose?

AI code is usually written for the case where everything goes right. Real users, however, do unexpected things.

• Submit empty fields
• Type nonsense into the email field
• Paste a very long text
• Click a button several times quickly

While building our own website, this is exactly how we found a bug where a mistyped phone number erased everything the user had typed in the form. We would never have noticed by only using it the "normal" way.

3. Are passwords or API keys written into the code?

Ask an AI to "build an email feature" and the example may hard-code an API key. Push that to GitHub and anyone can see the key.

• Put API keys and passwords in environment variables (a .env file, or your host's environment settings), not in code.
• Make sure .env is listed in .gitignore.
• If a key was exposed, do not just delete it — rotate it immediately.

4. Does the code trust whatever users send?

An input field is a channel anyone can send anything through. Even if the page blocks bad input, a determined person can bypass it.

• Check that inputs are validated again on the server (email format, length limits and so on).
• Code that pastes user input straight into a database query is dangerous. Ask the AI, "Is this safe against SQL injection?"

5. Does the library actually exist?

AI sometimes invents functions or package names that do not exist. They sound plausible, which makes them easy to fall for.

• If installation fails with "not found", search the official docs or package registry (npm, PyPI, etc.) yourself before asking the AI again.
• Be careful not to install a suspicious package with a similar name — a common trick used by malicious packages.

6. Is the AI's knowledge of the library up to date?

AI only knows what it learned up to its training cutoff. If a library has changed significantly since then, the AI may write code in an old style that no longer works.

• Check which version you have installed and tell the AI, "Write this for version X."
• For important features, compare against the official documentation for that version.

When we built our site on the latest version of a web framework, several rules had changed, so we checked the official docs before writing code.

7. Did you read the error messages all the way through?

A page can look fine while errors are happening in the background.

• On a website, open the browser's developer tools (F12) → Console and look for red errors.
• If there is an error, copy the entire message and show it to the AI. You will get a far better answer than with "it doesn't work".

8. Can you explain what the code does?

You do not need to understand every line. But you should be able to say "this part does this", or you will be stuck when something breaks later.

Try asking the AI:

• "Explain this code line by line as if I were a beginner."
• "Where could this code go wrong?"
• "How would this change if written a different way?"

9. If it handles personal data, did you check your legal obligations?

Even a single contact form that collects names, phone numbers and emails can create legal obligations, such as publishing a privacy policy. AI writes code; it does not take care of your legal duties.

• Write down what information you collect, why, and how long you keep it.
• Collect only what you truly need. Data you never collected cannot leak.

10. Can you roll back?

When working with AI, repeating "fix it" can sometimes make things worse.

• Use version control such as Git and commit every time things work.
• If something breaks, you can return to that point instantly. This alone makes working with AI much safer.

Summary checklist

• Ran it myself, including on a phone
• Tried invalid input on purpose
• No passwords or API keys in the code
• Inputs are validated again on the server
• Every library actually exists
• Checked the approach against the docs for my library version
• No errors in the console
• I can explain what the code does
• Personal data obligations checked
• Working states are saved

AI is an excellent assistant, but responsibility for the result stays with people. We hope this checklist helps you build more safely with AI.

If your project is hard to verify on your own, contact Springuptech. We build fast with AI and verify carefully with real-world experience.