Common failure patterns in AI-generated code, and the checks that catch them before they matter.
AI-generated code does fail. The hard part is that the failures often look like working output. The formatting is clean and the structure looks plausible. A human collaborator tends to signal uncertainty, hedging or flagging the parts they are less sure about. AI does none of that, so working code and fabricated code are delivered in the same polished tone.
There is a structural reason for this. The model does not check code the way a programmer or a compiler (the program that translates code into something the machine can run) would. It predicts what tokens (roughly, the next word or symbol) are likely to come next based on patterns in its training data, which is enough to produce plausible-looking output but not enough to guarantee the function exists, the library is real, or the logic is sound. Correct code and broken code are produced by the same prediction process, so the confidence level never varies.
AI confidence level vs. actual code quality:
The confidence dial is always at maximum, whether the code works or not.
AI-generated code tends to break in predictable ways. Once you know what to look for, you can check for those patterns specifically.
The AI generates code that imports libraries or calls functions that do not actually exist. The result can look completely plausible, with naming conventions that feel right and no visible sign that the package was never published. (A "package" or "library" is a bundle of pre-written code that other programs can reuse, distributed through a public registry where anyone can look it up.)
This is one of the easier failures to catch because the code will simply refuse to run. It can still waste time, though, if you build several layers of logic around the phantom library before discovering it does not exist. A quick check of the package registry early on heads that off.
This pattern is harder to catch precisely because the code is correct, or at least appears to be. It does what you asked, produces the expected output, and passes casual inspection. But it may leave security holes behind it: a way for a malicious user to inject commands into a database query, a missing login check, user-submitted data that gets rendered on the page without being cleaned first. There is no error message to tip you off, and the "does it run?" test that catches hallucinated APIs will not help here.
AI tends to generate code for the scenario where everything goes right: the database responds, the form field has a value, the file is exactly where it should be. When conditions are less cooperative, the code may fail silently or crash outright. You can often surface this by asking the AI directly: "What happens if the input is missing or malformed?" That single question forces it to confront the cases it skipped on the first pass.
AI models are trained on code from a specific time window, so they sometimes generate code using deprecated functions (ones the language or library has officially retired), outdated library versions, or patterns that the community has since replaced. The code might work today, but if the approach was abandoned because of a known security flaw, the AI will cheerfully reintroduce it. Asking the AI to check the current documentation for the libraries it used is a straightforward way to surface this kind of drift, and models with web access can do the lookup themselves.
When you repeatedly ask the AI to "fix this" without explaining the actual problem, each round gives the model another chance to take the path of least resistance. Sometimes that means removing the safety check that surfaced the error. The error message disappears, the code looks cleaner, and a protection you needed is gone. Models are getting better at holding context across rounds, but the risk still increases when you go several iterations without reviewing what actually changed.
The failure patterns above are not equally easy to catch. Hallucinated APIs reveal themselves the moment you try to install them, and outdated patterns often surface through deprecation warnings. Security problems produce no warning. The code runs, the output looks right, and you only find out a protection is missing when someone exploits it.
AI models are trained on feedback that rewards acceptance, meaning code the user does not reject. Security features add friction by design. They include authentication flows, input validation, and permission checks. An AI optimizing for acceptance may quietly strip away exactly the protections you need most, especially during a fix-it loop where you are pasting error messages and asking for quick resolutions. Documented examples give a sense of the pattern. A user reports a database connection error and the AI resolves it by making the database publicly accessible; a user asks to simplify a login flow and the AI removes the password requirement; a user asks to handle an API key error and the AI hardcodes the key directly in the source code, making it visible to anyone who viewed the file. Each of these "fixes" eliminated the error message. Each also eliminated a protection.
AI-generated code needs to be checked. The checks below focus on behavior and results, so they work even if you cannot read every line of the output. You do not need to memorize the specific tool names mentioned here, just know that these categories of tools exist and that you can ask the AI to set them up for you.
The vaguer your request, the more the AI has to guess, and guessing is where problems start. Before you prompt, write down what data goes in, what should come out, and what the boundaries are. If you are working with a specific library, say so; if your data has quirks, mention them. Breaking a complex project into small, testable pieces also helps, because smaller requests are easier to verify and debug than whole applications requested at once.
It also helps to make the tool read what is already there. If you are adding to an existing project, notebook, or codebase (the full collection of files that make up a piece of software), point it at the relevant files and ask it to understand the structure before generating anything. Left on its own, it will happily add a second CSV parser when one is already in use, or use one naming convention in a project that already uses another. You can say something as simple as "Read through the existing scripts in this project and tell me what libraries and patterns are already in use," or "This project already uses requests for HTTP calls, so stick with that."
Push back on the AI's decisions and make it explain itself. Ask why it chose a particular library, what tradeoffs the approach involves, what assumptions it is making about your data, and what could go wrong. If the answers are vague or circular, expect bugs to surface later. The question "Is there a simpler way to do this?" is particularly useful, since AI-generated code tends toward over-engineering.
Even if you cannot audit every line of code, the output is still available for verification. If the AI wrote a script to clean your dataset, spot-check the results: did it drop rows it should have kept, did it mangle any values, does a chart match what you know about the data? Compare row counts before and after, inspect a few records you already understand, and make the code prove itself on a case you could check by hand.
Go beyond informal "run it and hope" verification. Ask the AI to set up the testing and code-quality tools that professional developers use to catch bugs, and then ask it to check its own claims against current documentation. You can phrase these as direct instructions:
The point of these tools is to make verification a separate, deliberate step. A linter or test suite catches whole categories of bugs automatically, and current documentation catches the fabricated or outdated details that a model will present with full confidence.
How you describe a failure to the AI shapes the quality of the fix. This is where the fix-it loop from earlier becomes a practical concern: if you just paste an error and say "fix this," the model may quietly remove the check that surfaced the error. Share the full error message, describe what you expected versus what actually happened, include a sample of the real data, and say what you have already tried. Without that precision, the model will cheerfully fix the wrong problem.
Version control gives you a way to undo a bad fix. Commit working code after each successful change, and ideally before asking the AI to make another consequential edit, so a bad fix can be reversed without losing the working version you started from.
A short list of questions worth running through before accepting AI-generated code into your project.
None of these checks assume you can read code fluently. They assume you are willing to test the output against cases you already understand and remember that the tool will sometimes be confidently wrong.