What are AI hallucinations and why do chatbots make things up?

AI hallucinations happen when a language model generates text that sounds confident and plausible but is factually wrong or completely invented, and they occur because LLMs predict words based on statistical patterns rather than verifying truth. Here's the key thing to understand: an LLM's job is not to be correct. Its job is to produce text that looks like what a human would write. When you ask 'Who won the 2023 Nobel Prize in Literature?' the model doesn't check a database. It predicts the most likely sequence of words based on its training data. If its training data is incomplete or the question is unusual, it may generate a name that sounds right but isn't. A concrete example: I once asked an AI tool to summarize a research paper and it confidently cited statistics that weren't in the paper at all. The numbers were plausible, the format was correct, but they were fabricated. That's a classic hallucination. They're especially common when you ask about niche topics, recent events after the model's training cutoff, or specific people. According to research from Vectara published in 2024, hallucination rates vary significantly between models, with some producing inaccurate summaries up to 27% of the time on certain benchmarks. The practical tip: treat AI output like a knowledgeable intern who sometimes bluffs. For anything that matters — citations, statistics, legal details, medical advice — verify independently. You can also reduce hallucinations by giving the model source material and asking it to stick to that text only. For more on troubleshooting when AI gives you bad results, see our guide on [ChatGPT prompts not working](/blog/chatgpt-prompts-not-working). **Related**: How can I tell if an AI response is hallucinated? | Do all AI models hallucinate at the same rate?