{"id":7260,"date":"2026-06-18T07:26:16","date_gmt":"2026-06-18T07:26:16","guid":{"rendered":"https:\/\/hello-ai.eu\/how-do-you-prevent-hallucinations-in-ai\/"},"modified":"2026-08-05T12:23:49","modified_gmt":"2026-08-05T12:23:49","slug":"how-do-you-prevent-hallucinations-in-ai","status":"publish","type":"post","link":"https:\/\/hello-ai.eu\/en\/how-do-you-prevent-hallucinations-in-ai\/","title":{"rendered":"How do you prevent hallucinations in AI?"},"content":{"rendered":"\t\t
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Why AI Can Lie and How We Deal With It<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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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.<\/p>

Welcome to the fascinating, sometimes frustrating world of AI hallucinations<\/strong>.<\/p>

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?<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Why does AI sometimes make mistakes?<\/strong><\/h2>

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.<\/p>

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.<\/p>

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?”<\/p>

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.<\/p>

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Why does this happen?<\/strong><\/h3>

There are a few reasons why AI does this.<\/p>

First of all, AI works with pattern recognition. It knows, for example, that a sentence like “The Eiffel Tower was built in\u2026” is usually followed by a year. If the model isn’t sure of the exact year, it may fill one in anyway.<\/p>

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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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What is an AI hallucination?<\/strong><\/h2>

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.<\/strong><\/p>

The treacherous part is the tone. An AI rarely doubts out loud. It doesn’t say: “I’m guessing it’s this.”<\/em> No, it presents a complete lie with the conviction of a professor.<\/p>

Example:<\/strong> 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.<\/p>

Why does an AI hallucinate? (The back end of the algorithm)<\/strong><\/h3>

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<\/strong>.<\/p>

When you ask a question, the AI doesn’t search through a digital encyclopedia. It simply calculates what \u2013 based on statistics \u2013 the most logical next word<\/em> is in the sentence.<\/p>

There are a few main causes for that prediction going off the rails:<\/p>