Is This Normal? Reading the Warning Signs in AI-Generated Code

You opened up the code behind the app you built, scrolled through a file, and something made you pause. Maybe a function is 200 lines long. Maybe there's a comment that says // TODO: fix this later. Maybe a password or a key is sitting right there in plain text. Your gut says something is off, but you can't tell whether it's fine, a little sloppy, or the kind of thing that gets you in real trouble.

That "is this normal?" feeling is exactly the right instinct, and this post is here to help you triage it. The goal is simple: separate the harmless quirks from the patterns worth investigating, using nothing more than your eyes and a checklist. This is detection-first work. You're learning to spot and flag things, not to fix them blind or poke at anything to see what breaks. If you want the wider context first, start with how to understand the app you built with AI, which this post supports.

Why AI-generated code has predictable quirks

AI coding tools generate code by predicting the most likely next piece based on patterns in a huge amount of existing code. That has two consequences worth knowing.

First, the output is usually plausible. It follows common conventions, so most of what you see genuinely is normal. That's reassuring.

Second, the tool optimizes for producing something that runs and looks reasonable, not for the specific safety, privacy, and maintenance needs of your particular app. It doesn't know your app handles real customer emails, or that a shortcut it took in a demo is a bad idea in something people actually use. So the quirks it leaves behind tend to fall into predictable buckets. Once you know the buckets, the whole thing gets less mysterious.

Research on AI-generated code quality generally points in a consistent direction: the code often works but can carry a meaningful rate of security and maintainability issues, and those issues are easy to miss because the surrounding code looks confident and clean. Treat that as a reason to look, not a reason to panic.

Benign patterns: things that look odd but are usually fine

A lot of what makes new builders nervous is cosmetic. Here are common examples that, on their own, are usually harmless.

• Verbose or over-commented code. AI tools love explanatory comments and long variable names. Reading numberOfActiveUsersThisMonth instead of n looks fussy, but it's not a problem. It's often easier to read.
• Repeated code instead of one shared function. The same snippet copy-pasted in three places is untidy and slightly harder to maintain, but it's not dangerous. It's a cleanup task, not a red flag.
• Extra dependencies or imports. A library pulled in for one small thing is common. Worth noting for later, not an emergency.
• Defensive checks that seem redundant. Code that checks "does this exist?" three times can look paranoid. Paranoid code is usually the safe kind.
• Placeholder text and sample data. Names like John Doe, test@example.com, or Lorem ipsum left in a non-user-facing spot are just leftovers.
• Inconsistent style. Different files formatted differently, mixed quote marks, uneven spacing. Cosmetic. A formatter fixes it in seconds.
• Long functions. A single function doing a lot is harder to read, but length alone doesn't make code unsafe.

The theme: benign patterns cost you readability and future effort, not safety. If the worst outcome is "this will be slightly annoying to change later," you're looking at a quirk, not a crisis.

Concerning patterns: signs worth investigating

Now the other bucket. These are patterns worth flagging and looking into further. I'm describing how to recognize them, not how to exploit them or how to make an error go away by ripping a safeguard out. If you spot these, the right move is to write them down and, in several cases below, get expert eyes.

• Secrets sitting in the code. API keys, passwords, database connection strings, or private tokens written directly into files (rather than stored in a separate configuration or environment setting) are a well-known concern. If you can read a secret in plain text in your code, so can anyone who gets a copy of that code.
• Personal data handled casually. Emails, names, addresses, or payment details that appear to be stored or logged in plain text, or sent somewhere without obvious protection, deserve a closer look.
• Missing checks on user input. Code that takes whatever a user types and uses it directly, without any validation, is a classic source of trouble. You don't need to know the attack. You just need to notice the absence of a check.
• Authentication that looks improvised. Login or permission logic that seems hand-rolled, hard to follow, or full of special cases is worth flagging. Auth is easy to get subtly wrong.
• "Allow everything" settings. Configuration that appears to open access widely (permissions set to the broadest option, or comments like "allow all") is a detection signal to investigate, not something to leave assumed-fine.
• Error handling that hides problems. Code that catches an error and silently does nothing can mask real failures. Empty "catch" blocks are a smell.
• Copy-pasted security code the tool clearly guessed at. If a block dealing with passwords, tokens, or encryption looks like it was pattern-matched from somewhere and nobody understands it, that's worth a professional's read.

For a deeper catalog of these, see security mistakes AI puts in your code. The point here isn't to alarm you. Finding one of these is common and fixable. It's a to-do item, not a verdict on you as a builder.

How to check without changing anything you don't understand

The safest way to investigate is to observe, not edit. You can learn a lot without touching a line.

1. Search, don't scroll. Use your editor's search across all files for words like password, secret, api_key, token, TODO, FIXME, and catch. This surfaces the concerning patterns fast without you reading every file.
2. Read the flow, not the syntax. For anything that looks off, ask: where does this data come from, and where does it go? You're tracing the path, not judging the punctuation. If you're new to this, how to read code you didn't write walks through the technique.
3. Write down what you find, verbatim. Note the file name and a short description: "auth.js, login check looks custom and hard to follow." A clear list is what a professional needs, and it stops you from acting on a half-understanding.
4. Ask the AI tool to explain, not to fix. You can paste a snippet and ask "what does this do and what are the risks?" Use the answer as a lead to verify, not as gospel. Confirm anything important against the tool's or platform's own documentation.
5. Don't disable things to test them. Turning off a validation or a login check to "see if that's the problem" can quietly expose real data. If understanding a piece requires switching off a safeguard, that's your signal to stop and get help instead.

The security self-audit gives you a structured version of this pass so you're not relying on memory. Treat it as a detection aid that helps you find and organize issues, not a tool that makes your app secure.

When "concerning" means stop and call a professional

Some findings are fine to sit with and learn about. Others mean you should stop investigating on your own and bring in someone qualified. Here's a clear threshold.

Get a professional involved when any concerning pattern touches:

• Logins and accounts (authentication). Anything controlling who can sign in and what they can access.
• Payments. Any code that takes card details, processes charges, or connects to a payment provider.
• Personal data. Anything that stores, moves, or logs real people's names, emails, addresses, health or financial details.

These three areas are where a mistake stops being a cleanup task and becomes a real risk to your users and to you. You don't need to have proven there's a problem. Reasonable doubt in these areas is enough to justify a paid hour of expert time. When to hire a security professional covers how to find one and what to ask so you're not overpaying or under-scoping.

A useful rule of thumb: if fixing it wrong could expose a user's data, charge someone incorrectly, or let the wrong person in, don't experiment. Detect, document, and hand it over.

This section is educational, not legal advice. If your app handles payments or personal data, requirements vary by where you and your users are located, and you should verify your specific obligations with a qualified professional.

What the research suggests about AI code quality

The broad, directional takeaway from work on AI-generated code is worth holding in mind: these tools produce working code most of the time, but a non-trivial share of output can contain security weaknesses or maintainability problems, and people tend to trust confident-looking code more than they should. I'm deliberately not quoting a percentage, because the figures depend heavily on the tool, the task, and how the study was run, and I don't want to hand you a number that doesn't apply to your situation.

The practical version is the whole point of this post. Assume some quirks. Learn which bucket each one falls into. Flag the concerning ones. Escalate the ones near logins, payments, and personal data. That habit protects you far better than any single statistic.

In Plain English

What this means: Most odd-looking things in AI-generated code are cosmetic and safe. A smaller set (secrets in plain text, casual handling of personal data, missing input checks, improvised auth, wide-open permissions, silent error handling) are worth investigating. You can spot all of them by looking and searching, without changing anything.

Who this helps: Solo founders and non-technical builders who shipped an app with AI or no-code tools and now feel uneasy about something in the code but can't tell how serious it is.

When to use it: Any time you open your code and get that "is this normal?" feeling, or before you launch, share access, or start handling real user data.

What to do next: Do a detection pass, write down what you find, and sort each item into benign (fix later) or concerning (investigate). For anything that touches logins, payments, or personal data, stop and get a professional's read.

Your next step

If your gut is telling you something's off, give it a structured look rather than losing sleep over it. Run the security self-audit to work through your code methodically, and browse common fixes to see how builders handle the patterns you'll likely find. Both are detection and learning aids, not guarantees that your app is secure. When you want the bigger picture of owning what you built, head back to Understand what you built. Finding a red flag isn't a failure. It's the moment you start actually owning your app.

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Kodbe is an independent commercial publication about maintaining and owning software built with AI or no-code tools. It is not affiliated with Kodbe Inc, Kodobe, Kodable, or Kodu.