You probably know the feeling. You ask an AI chatbot for a specific source for your thesis, or for a summary of an obscure court case. The answer rolls out smoothly. Beautiful sentence structure, convincing tone, complete with page numbers and links. You check it… and it turns out to be completely wrong. The source doesn’t exist, the court case is made up.
Welcome to the fascinating, sometimes frustrating world of AI hallucinations.
Now that artificial intelligence has become a fixed part of our daily workflow, we’re increasingly running into these digital mirages. But what exactly is an AI hallucination? Why does a machine that cost billions to build act as if it knows everything, and how do you spot the fiction among the facts?
AI can do a great many things well, but there’s also an important side to AI that you need to take into account. AI can also make mistakes.
When a language model like Claude, Gemini, or ChatGPT makes up something that isn’t accurate, a fact, a name, a quote, a source reference, we call that a hallucination.
But “making up” is actually still a bit too human a term to use for AI. AI predicts, word by word, what the most logical continuation is. It doesn’t actually check whether it’s all really correct. There’s no built-in checker that actually verifies the facts and asks, “Is this actually correct?”
The result is that you get a complete text that looks really good and comes across as reliable, but that isn’t actually accurate in terms of content.
There are a few reasons why AI does this.
First of all, AI works with pattern recognition. It knows, for example, that a sentence like “The Eiffel Tower was built in…” is usually followed by a year. If the model isn’t sure of the exact year, it may fill one in anyway.
In addition, AI is trained to always give an answer. Instead of saying “I don’t know,” the model sometimes opts for an answer that sounds logical. The way of writing also plays a role. AI has learned what an article, report or Wikipedia text looks like. It can perfectly mimic that style, even if the content isn’t entirely accurate.
In psychology, a hallucination is the perception of something that isn’t there. With Large Language Models (LLMs), it’s slightly different. An AI hallucination is the phenomenon where the model confidently generates output that is factually incorrect, has no basis in the training data, or simply makes no sense.
The treacherous part is the tone. An AI rarely doubts out loud. It doesn’t say: “I’m guessing it’s this.” No, it presents a complete lie with the conviction of a professor.
Example: A chatbot that provides a complete recipe for deadly chlorine gas because it creatively combines patterns of ‘cleaning’ and ‘recipes’, or a lawyer who gets fined because he used case law that the AI made up on the spot.
To understand why AI lies, we need to understand how it thinks. An LLM is essentially not a database like Google. It’s an extremely advanced prediction machine.
When you ask a question, the AI doesn’t search through a digital encyclopedia. It simply calculates what – based on statistics – the most logical next word is in the sentence.
There are a few main causes for that prediction going off the rails:
It’s easy to view hallucinations as a pure software flaw that needs to be ‘fixed’ as quickly as possible. But tech experts and AI researchers have now reached a consensus: hallucinations are a by-product of the probabilistic nature of AI. If we were to eliminate hallucinations 100%, we would also strip the creativity out of the model. After all, the same ‘freedom’ that causes an AI to invent a fake URL also enables it to write an original science fiction story, brainstorm marketing campaigns, or come up with unique metaphors.
Completely error-free AI does not (yet) exist, but you can drastically reduce the chance of hallucinations. Here are the most important rules of thumb:
If you use AI for business purposes, force the model to draw from a specific database (for example, your own HR handbook or document set) instead of its general knowledge. In your prompt, you literally say: “Use only the text below to answer the question. If the answer is not in it, say ‘I don’t know’.”
Don’t ask: “What happened in the tech world last year?” Instead, ask: “What were the three biggest acquisitions within the Dutch AI sector in 2025? Only mention verifiable facts.” The tighter the framework, the less room the AI has to wander.
The most important rule remains: never blindly trust critical output. Use AI as a brilliant, lightning-fast intern. The intern does the groundwork, but you are the senior who checks the facts, clicks the links, and double-checks the numbers before it goes public.
Not all information is equally reliable when using AI. There are a number of things models get wrong more often:
It’s especially important to be extra critical with this type of information.
Which model doesn’t do this?
There is no model on the market that doesn’t hallucinate. Claude does it to a lesser extent than other models, and Anthropic invests heavily in techniques such as RLHF (RLHF stands for Reinforcement Learning from Human Feedback). It’s the technique used to fine-tune modern language models after they’ve first been trained on large amounts of text. And they use Constitutional AI to limit it, but it’s still far from error-free.
Some models try to signal when they’re not sure about something, but even that signal isn’t reliable. It’s a fundamental problem with how language models work, and developers can’t solve this.
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