If you have used an AI coding assistant for more than a week, you have probably hit this exact moment. It suggests a function, the code looks clean, the naming convention feels right, autocomplete even agrees with it, and then you run it and get an error saying that function does not exist anywhere in the library you are using. You go check the documentation and it is genuinely not there. It was never there.
This is called an AI hallucination, and it is not a rare glitch. It is one of the most well documented and actively studied problems in how large language models work. If you use AI tools daily as part of your workflow, understanding why this happens will save you real debugging hours and stop you from shipping broken code with confidence.
What is actually happening when a model hallucinates
To understand this, it helps to know what a large language model is actually doing when it writes code for you. It is not looking anything up in a database. It has not opened your project's dependencies and checked what functions are actually available. What it is doing is predicting the next most likely piece of text, one token at a time, based on patterns it learned from an enormous amount of code and text during training.
If you ask it to write something using a popular library, it has seen thousands of examples of that library being used. It has learned the general shape of how that library's functions are usually named and called. So when it generates a new function name that fits that shape, it sounds completely plausible even if that specific function was never actually written by anyone. The model has no internal step that checks against the real, current source code of the library. It only checks whether the output looks like the kind of thing that would be correct.
This is why hallucinated code is so convincing. It is not random nonsense. It follows the exact naming conventions, argument patterns, and style of the real library. That is exactly what makes it dangerous, because it does not look wrong until you actually run it.
Where this shows up constantly in real development work
There are a few specific patterns worth knowing so you can catch them faster.
Hallucinated library methods are the most common. The model suggests a function name that sounds exactly like something that should exist in that library, matching its naming style perfectly, but it simply is not there in the version you have installed.
Wrong or outdated API parameters happen when a model confidently tells you an endpoint accepts a field that was deprecated two major versions ago, or renamed, or never existed in the first place. This is especially common with fast moving frameworks and cloud provider SDKs that change frequently.
Fabricated documentation references occur when the model tells you to check a specific section or page of the official docs, complete with a plausible sounding heading, and that page simply does not exist.
Version confusion is subtle and frustrating. The suggested code might be completely correct, but for an older version of the framework, and it silently breaks in the version you are actually running without any obvious error explaining why.
A quick example of what this looks like in practice
Imagine asking an AI assistant to fetch paginated results from an API client. It might confidently generate something like:
results = client.fetch_all(cursor=None, page_size=50)
This looks completely reasonable. The naming is consistent with how pagination usually works. But if you check the actual library, the real method might be called list_items with a completely different parameter structure. The AI did not make an error in logic. It generated the statistically most likely looking function call based on patterns from similar libraries it had seen before, not from the actual source code of the one you are using.
What actually helps when working with these tools
Treat any AI suggested function, method, or API call as unverified until you have checked it against the actual installed version's documentation, not just your general memory of how that library usually works. Running the code and reading the real error message is very often faster than trying to reason your way through whether a suggestion sounds correct.
If you are building AI assisted tooling for other developers, wiring in retrieval augmented generation so the model pulls from your actual current documentation at the time of the request meaningfully cuts down on this problem, instead of relying purely on what it memorized during training, which may already be outdated by the time you are using it.
The bigger point for anyone working with AI tools daily
None of this means AI coding tools are not useful. They clearly speed up a huge amount of work. It just means treating their output the way you would treat a pull request from a very fast, very confident junior developer. Often right, genuinely useful, but never something you merge without checking it actually works against your real codebase and your real dependencies.
Understanding this distinction is quickly becoming a basic skill for any developer working alongside AI tools, not an advanced one.
(https://tuxacademy.substack.com/p/your-ai-just-lied-to-you-with-a-straight)
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