How to Review AI-Generated Code When You Are Not a Programmer

The hardest part of using AI to build your first app is not writing the prompts — it is knowing whether the output is any good. Every feature that works feels like a win, but the nagging question stays the same: did the AI take a shortcut you will regret later? The good news is you do not need a computer science degree to answer that question. You need a review process designed for what you already know — your product, your users, and your own common sense.

What you bring that the AI cannot

When you review AI-generated code, the balance of power is not what you might think. The AI knows syntax and framework APIs. You know context — how the app should behave, who uses it, and what matters when things go wrong.

This table is your review toolkit. Every item in the left column is a check you can run without opening a debugger. The key insight is that most AI coding mistakes are not syntax errors — they are logic errors, missing edge cases, and hallucinated features. Those are exactly the kind of problems your non-technical intuition is good at catching.

The GitHub blog on AI-generated code quality makes a similar point: the most dangerous bugs in AI output are not compilation errors but subtle logic mistakes that look correct at first glance.

Ask the AI to explain itself in plain English

Before you dig into testing, ask the AI to summarise what it built in plain language. This is the single most effective review technique for non-programmers.

Explain what changes you made in this code, in plain English. Do not use jargon. List each file you created or modified and what it does. If you made any assumptions about how the app should behave, list those too.

When the AI explains its own code, you can check the description against your requirements. Did it build what you asked for? Did it add extra features you did not request? Did it make assumptions about your data that do not match reality? If the summary surprises you, the code probably needs changes too.

This technique works because the model encodes its reasoning in the explanation. A summary that glosses over edge cases or skips the hard parts often points to gaps in the implementation. You do not need to read a single line of code to spot that disconnect.

Test like a user, not a debugger

You already know how to test software. You use apps every day. The same instinct that tells you a button is in the wrong place or an error message is unhelpful is exactly what catches AI coding mistakes.

Run through these five scenarios:

1. Happy path. Does the main feature work when everything goes right?
2. Empty state. What happens when there is no data? A blank white screen is a red flag.
3. Error state. Submit a form with empty fields, load a page with a broken link, or enter text where a number is expected.
4. Edge inputs. Try the longest name you can think of, special characters, or pasting a paragraph into a single-line field.
5. Refresh test. Does the app still work after you refresh the page? Many AI-generated apps lose state on reload.

Each of these tests can reveal a bug the AI did not handle. Report what you find back to the model in a follow-up prompt — describe what you did, what you expected, and what actually happened. The AI will often fix the issue in a single pass.

The Vibe Coding for Beginners guide devotes a full chapter to these testing patterns, with prompt templates you can copy for each scenario.

Spot the warning signs without reading code

Some AI mistakes leave visible fingerprints even when you cannot read the syntax. Train yourself to notice these patterns in what the AI generates:

• Placeholder content. “Lorem ipsum”, “Your name here”, and “[email protected]” are signs the AI generated demo text instead of your real content.
• Hard-coded values. Numbers, API keys, and URLs that appear directly in the code instead of living in a configuration section. Ask the AI to extract every hard-coded value into a settings block at the top.
• Unrealistic demo data. Sample records that look plausible but do not match your real data structure. The AI may invent field names, categories, or relationships that do not exist.
• Repetitive blocks. A long stretch of similar-looking code usually means the AI missed an opportunity to use a loop (repeat a block of code efficiently). You can flag this without knowing the language.

A concrete example. You ask for a product catalogue and the AI generates an HTML table with sample data:

<table>
  <tr>
    <th>Item</th>
    <th>Price</th>
    <th>In Stock</th>
  </tr>
  <tr>
    <td>Widget A</td>
    <td>£9.99</td>
    <td>Yes</td>
  </tr>
  <tr>
    <td>Widget B</td>
    <td>£14.99</td>
    <td>Yes</td>
  </tr>
</table>

The HTML is valid. The table renders fine. But the data is fake — those products do not exist in your inventory. A non-programmer catches this immediately because they know their own business. The fix is a prompt that tells the AI to use your real product data.

Ask the AI to review itself

Once you have run your tests and noted anything suspicious, hand the list back to the AI with a structured self-review request:

Review the code you just generated and check for these issues:

1. Are there any hard-coded values that should be configurable?
2. Does every user-facing message use British English spelling?
3. Is there any placeholder or demo content that needs replacing?
4. Are there error states for empty data, network failures, and invalid input?
5. Are there any security concerns like exposed API keys or missing input validation?

List each issue you find and propose a fix for each one.

The AI is surprisingly good at critiquing its own output. It can identify patterns it would not change unprompted — a tendency known in AI research as sycophancy, where models agree with the user rather than pointing out problems. Asking directly for a self-review switches the model into a critical mode and surfaces issues it would otherwise gloss over.

Build confidence one review at a time

Every review cycle makes the next one faster. You learn which patterns your AI tool tends to miss, which prompts produce clean output on the first pass, and which types of check catch the most bugs.

Over time, you will develop a review instinct that comes from knowing your project — not from learning to code. That instinct catches the bugs that matter to your users, and it is far more valuable than any syntax knowledge you could acquire in a weekend course.

For a broader walkthrough of building AI-assisted projects from scratch, see A Simple Guide to AI Coding. If you are still finding your feet with the prompt side of the process, Vibe Coding for Beginners covers everything from your first instruction to your first deployed page.