That's abusing a turn-of-phrase to provide a counter-argument. Of course no one can guarantee universal factuality. But relative to information we're seeking from an assistant (schedules, email data, messages, music preferences) it is very easy to obtain highly accurate data. But LLMs don't work like that.

The conceptual model behind LLMs utilizes frequency to generate a reasonable response to a query. But that's a bad fit when you're looking for data accuracy that isn't represented by the model. If your model says most dental appointments occur on the 15th and responds so but my appointment is on the 17th, it's not only useless to me it's detrimental.