Why AI-written code has bugs — 3 causes, and why it doesn't mean 'AI is dumb'

You ask the AI for a chunk of code. It hands back something confident, neat, nicely formatted — and it crashes with an error or does the wrong thing. Annoying and confusing: it looked perfect. Here's what's worth getting straight from the start: the problem isn't that the AI is "dumb." It's how it works. Once you get the three causes below, you'll stop being surprised by bugs and start preventing them.

The symptom

The model's code looks competent, but: it throws an error, calls a function that doesn't exist, quietly does something other than asked, or breaks what already worked. Let's unpack where that comes from.

Cause 1. The model guesses the plausible, not the correct

The AI doesn't "know" code. It predicts which text most likely comes next, drawing on millions of examples. Usually plausible and correct line up — which is why it helps so often. But sometimes it confidently invents a function or library that doesn't exist: it looks real because "people write things like this." That's a hallucination, just in code.

How to check: an error like is not a function, module not found, no such method. Search for the function's name — if it's nowhere in the docs, the model made it up.

How to fix: say it straight: "that function doesn't exist, use library X's real API" — and give a link to the docs. The model leans on a fact instead of a guess.

Cause 2. It can't see your whole project

Unlike you, the model isn't "open" across all your files. It sees only what you put in the prompt. If you didn't show your functions, versions, and structure, it writes code in a vacuum — for some average project, not yours.

How to check: the code is fine on its own but doesn't fit yours — different names, different style, calls things you don't have.

How to fix: give context. Paste in the relevant file, name the stack and versions, show a similar working snippet. The more the model sees of your project, the less it makes up.

Cause 3. The task was vague — so the model filled the gaps for you

"Make the form nice" or "fix this" is too vague. Wherever you under-specified, the model fills in. And it often fills in something other than what you meant. You get working code that solves the wrong problem.

How to check: no errors, but it behaves differently than you expected. A sign of a misunderstanding, not a bug.

How to fix: be specific — what goes in, what comes out, what the constraints are. That's half the battle; it's covered in how to write a good prompt.

So can't I rely on AI code at all?

You can — but like a strong junior's draft, not a finished solution. The golden rule: ask in small chunks, read what comes back, and run it right away. Don't dump everything in one giant request — that's where bugs hide. It also helps to understand the code yourself: how to read AI-generated code.

Will this get better with newer models?

Partly. Models really do invent less and handle code more carefully year over year. But the first two causes — guessing and incomplete context — are baked into the nature of the tool. So the habit of giving context and checking stays useful forever.

Why is the AI so confident even when it's wrong?

Because the confidence in its tone and the correctness of its answer aren't connected. It writes correct and invented code with the same smoothness. Don't trust the tone — trust the run.