AI hallucinations explained in 2026: what they are, why they happen, how often they show up, and what you can do to reduce the risk.An AI hallucination is when an AI model makes something up: fake facts, fake citations, fake studies, or real things with wrong details. In this video, I break down clear examples (including a made-up “idiom” that ChatGPT confidently explained as real), plus public cases where AI hallucinations created serious consequences: fabricated book lists, lawyers citing court cases that never happened, and high-stakes decisions made on confident-sounding output.You’ll learn why large language models hallucinate in the first place: they aren’t looking up truth from a database (and it's not as simple as saying "it's a feature, not a bug"). They generate text by predicting likely next words, and they’re rewarded for answering fluently, not for being honest about uncertainty. That makes errors feel persuasive, especially when you’re tired, rushed, or already leaning toward a conclusion.We also look at how common AI hallucinations still are, using recent benchmarking, and why “just trust the model” is not a strategy, particularly in legal, medical, financial, or reputational contexts.Finally, I walk through practical risk controls to keep you safe from the consequences of AI hallucinations: treating outputs as drafts, verifying anything consequential, setting human checkpoints and accountability inside teams, and using approaches like retrieval (RAG) to ground responses in sources you control. Hallucinations won’t fully disappear, so you need processes that assume they will happen.Links:AI Edit: https://www.theaiedit.ai/ (for all my content on AI concepts explained easily)AI hallucination benchmark study: https://research.aimultiple.com/ai-hallucination/OpenAI paper (Sep 2025): https://openai.com/index/why-language-models-hallucinate/WTF is an LLM Anyway (video):

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