An LLM doesn't look facts up. It predicts the next token.

One word at a time, weighted by patterns it saw in training.

That single fact explains the failure everyone keeps hitting. When a model invents a drug interaction that sounds right, it isn't malfunctioning. It's doing the only thing it ever does, picking a plausible next word. Plausible and true only usually overlap.

Once you see that, "why did it make this up" stops being mysterious, and "where do we require a human to check" becomes the real design question.