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LLM “hallucinations” are only errors from a user expectations perspective. The actual purpose of these models is to generate natural-sounding language, not to provide factual answers. We often forget that - they were never designed as knowledge engines or reasoning tools.
The fact that they often get things right isn’t because they “know” anything - it’s a side effect of being trained on data that contains a lot of correct information. So when they get things wrong, it’s not a bug in the traditional sense - it’s just the model doing what it was designed to do: predict likely word sequences, not truth. Calling that a “hallucination” isn’t marketing spin - it’s a useful way to describe confident output that isn’t grounded in reality.
Whatever they were designed for, they are currently being sold as the solution to nearly every problem. You can't expect a layperson to look further than that, and it's completely reasonable to judge what they do against the claims being used to sell them.
You can blame the marketing departments, but that original purpose you mention is no longer a major talking point (even if it should be).