A lot of people use the term “AI hallucination,” but many don’t clearly understand what it actually means. In simple terms, AI hallucination is when a model produces information that sounds confident and well-structured, but is actually incorrect, fabricated, or impossible to verify. This includes things like made-up academic papers, fake book references, invented historical facts, or technical explanations that look right on the surface but fall apart under real checking. The real danger is not that it gets things wrong — it’s that it often gets them wrong in a way that sounds extremely convincing. Most people assume hallucination is just a bug that engineers haven’t fully fixed yet. In reality, it’s a natural side effect of how large language models work at a fundamental level. These sys...
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