How to review AI-written code if you're not a programmer
This scenario is becoming common: a business owner builds a prototype in ChatGPT or Claude, or receives a finished system from a freelancer who relied heavily on AI during development. The interface loads, the buttons work, data is saved — it looks like the product is ready. Then comes a question with no obvious answer: how do you know you can trust this code with real orders, payments, or customer communications if you can't read the code yourself?
The good news is that you don't need to know how to code to run basic checks. You just need to know how to ask the right questions and understand which answers should raise red flags. Below is not a technical manual, but a set of checks available to any business owner that separate a working demo environment from a system ready for real-world load.
"Works in demo" and "works for customers" are two different things
A demo usually follows a script: the right data is entered, buttons are clicked in the expected order, and the internet doesn't drop. AI tools are especially good at quickly assembling code that passes exactly this kind of scenario — that's precisely what they were trained on. But what happens when a customer enters letters in a phone number field, impatiently double-clicks "Pay," or two orders come in at the exact same time for the same item in stock — that's a separate issue, and AI often doesn't solve it on its own unless explicitly asked.
A simple check: ask the person who built the system to show it not in demo mode, but with intentionally "bad" actions — a double click, an empty field, weird input. If the developer shrugs or says "that won't happen," it's not a minor detail. Non-standard situations are exactly what causes real orders to be lost.
Has anyone other than the author looked at the code?
In standard development, there's a practice called code review: a second person reads the code before it goes live, looking for things the author might have missed — not out of malice, but because the author's own solutions seem obvious to them. With AI-written code, this is even more important: the tool might generate a working but messy or overly complex solution, and the only person who has seen it is the one who clicked "Generate."
You don't need to read the code yourself to ask: "Did anyone else look at this before delivery?" If the same person wrote and "accepted" the code without an outside perspective — it's not necessarily a disaster for a simple landing page, but it is a solid reason to request an independent review before real money or customer personal data starts flowing through the system.
Is there a place where you can break things without consequences?
Production is what real customers see right now. A test (staging) environment is a separate copy of the system where you can click any button, enter any data, and even break things without risking anything. If your system doesn't have such a copy and every change goes straight to production — sooner or later, a bug missed during editing will end up in front of a customer at the worst possible moment.
Ask directly: "Where do you test changes before they go to my website / bot / CRM?" If the answer is "nowhere, we edit the live site directly," that might be tolerable for a simple business card site, but for anything that takes orders or payments, it's a reason to insist on a separate test environment.
Who is responsible for security and customer data?
AI writes code that performs a task, but it doesn't think about how it can be bypassed or hacked — that's not part of the "build an order form" prompt. Typical vulnerabilities look harmless: passwords stored in plain text, any user having access to other people's orders via a tweaked URL, payment system keys sitting right in the code that later accidentally ends up in a public repository.
This isn't a reason to ditch AI in development — many of these issues can be fixed by a specialist in an hour. It is a reason to ask directly who is responsible for this part before launch, not after a leak. One more thing to keep in mind: if real customer data or credentials were used in the chat with the AI code generation tool — that's a risk in itself, regardless of the final code quality.
Frequently asked questions
If the prototype has been running without glitches for a week, does that mean it's secure?
Not necessarily. A week of glitch-free regular operation tells you nothing about what will happen under peak load, non-standard input, or a hacking attempt — these are entirely different types of testing.
Is it even worth hiring a freelancer who heavily uses AI for development?
Yes, it's a normal and increasingly common practice that speeds up work and cuts costs. The issue isn't the tool, but whether the contractor checks the result just as thoroughly as if they had written the code by hand.
Can you find vulnerabilities in the code yourself without technical knowledge?
Reading the code — no, but asking the questions listed in the article and requiring clear answers — you can and should. For a more thorough check before a major launch, it’s best to bring in a specialist for a one-time audit.