Yes, most people who disagree with this have no clear understanding of how a LLM works. It is just a prediction mechanism for the next token. The implementation is very fancy and takes a lot of training, but it's not doing anything more than next token prediction. That's why it is incapable of doing any reasoning.

Yeah its an interesting question, and I'm a little surprised I got down voted here.

I wouldn't expect them to add an additional LLM layer unless hallucinations from the underlying LLM aren't acceptable, and in this case that means it is unacceptable enough to cost them users and money.

Adding a check/audit layer, even if it would work, is expensive both financially and computationally. I'm not sold that it would actually work, but I just don't think they've had enough reason to really give it a solid effort yet either.

Edit: as far as fact checking, I'm not sure why it would be impossible. An LLM wouldn't likely be able to run a check against a pre-trained model of "truth," but that isn't the only option. An LLM should be able to mimic what a human would do, interpret the response and search a live dataset of sources considered believable. Throw a budget of resources at processing the search results and have the LLM decide if the original response isn't backed up, or contradicts the source entirely.