8 Common Bugs in AI-Generated Code (and How to Fix Them)

When a human writes a bug, it tends to be a one-off — a typo, a misread requirement. AI-generated code fails differently: the same categories of mistake recur across projects and models. That's frustrating, but it's also useful, because a predictable failure is one you can build a checklist against.

Here are eight of the most common patterns and how to handle each.

1. Imports for packages you don't have

Models happily reach for a library that fits the problem, whether or not it's installed. You get an import at the top of the file that fails immediately.

Fix: Check the imports first when something won't run. Either install the package or ask the AI to solve the problem with dependencies you already have.

2. Calls to functions and APIs that don't exist

An AI can invent a method that sounds exactly right but was never part of the library. This is sometimes called a hallucinated API.

Fix: Cross-check unfamiliar method names against the official docs. If the AI insists a function exists and it doesn't, paste the real documentation into the conversation as context.

3. Missing error handling

Unless you ask for it, generated code often assumes the happy path: the network call succeeds, the input is valid, the file is there. In production, none of those are guaranteed.

Fix: Explicitly prompt for error handling, and make each failure say what went wrong and what happens next. A good rule of thumb: every function that can fail should make its failure modes obvious.

4. Silent logic errors

The code runs, throws no error, and returns the wrong answer. These are the most dangerous because nothing flags them.

Fix: Test with real inputs and known expected outputs, not just "does it run." Break complex functions into smaller pieces you can check individually.

5. Unhandled edge cases

Empty lists, zero, negative numbers, missing fields, the very first item, the very last — models generalize from typical cases and skip the corners.

Fix: Write down the edge cases for your specific problem before you trust the output, then test each one. Ask the AI directly: "what inputs would break this?"

6. Outdated patterns

A model's knowledge has a cutoff. It may suggest a deprecated approach or an older way of doing something that has since been replaced — common with fast-moving frameworks.

Fix: When you know the current best practice, state it in the prompt. When you don't, verify the pattern against recent docs before building on it.

7. Skipped security basics

Generated code may not sanitize input, may expose secrets, or may use a weak default — unless you ask. A meaningful share of AI-generated code has been found to contain security flaws.

Fix: Treat security as an explicit requirement, not an afterthought. Ask for input validation, safe handling of credentials, and a review of obvious risks.

8. Code that works in isolation but breaks on integration

Each generated piece looks fine alone, then they don't fit together — mismatched data shapes, conflicting assumptions, an interface contract that doesn't line up.

Fix: Integrate and test in small increments rather than generating a whole system and wiring it up at the end.

Debug manually or regenerate? For small issues — imports, typos, a missing null check — fixing by hand is faster. For a fundamentally wrong approach, regenerating with a clearer prompt usually beats patching. If you've fed the same error back twice and it's still broken, start fresh with more context.

The through-line

Almost every fix above comes down to the same habit: run the code and watch what it actually does, on real and awkward inputs, before you trust it. That's exactly the loop a good testing workflow is built around.