Full Explanation
LLMs are optimized to produce fluent, grammatically correct, structurally coherent language. This is what they were trained to do. But fluency and accuracy are two different things. The model does not stop and verify whether what it says is true -- it generates the most likely continuation of text, token by token. The result is an AI that consistently sounds confident, even when the underlying content is wrong or outdated. This cognitive pattern is called fluency bias: we mistake the quality of the language for the quality of the information.
The practical implication is simple but important. When evaluating an AI answer, ignore the tone of certainty. Look instead at evidence, sources, and consistency. A perfectly written answer can still contain a perfectly written mistake. AI is optimized to sound right -- not necessarily to be right.
