Earlier quoted context omitted.
You can reduce hallucinations with appropriate grounding checks. You can’t really get rid of them though, so feedback loops with verification are essential.
Error percentages compound. Having an LLM do something with a 5% hallucination rate, then having an LLM (same or different) with a 5% error rate check it means there's a 10% chance of error, not .25%. Prompting can't save you here; it's the fundamental math.
Two independent task? You have a 95% chance of each task being correct, so total success is .95^2 = 90.25% correct.
Having two AIs verify the same task with each being 95% correct? That's a 5% error per each, so 1-(.05^2) = 99.75% correct.
That is assuming fully independent error rates, which is the real issue. If it is pure hallucinations, then that is an okay assumption, but if there are issues in the underlying prompt/data causing the error rates, then it becomes a very dangerous assumption and I think more human involvement is recommended to better understand and resolve the underlying data problem.
The real interesting bit is that more AI's mean the 2 in the above equations gets bigger, so the error rate gets worse or better depending upon which equation is in play. So if you are in an in-between landscape where you aren't sure there is independence or not (or how much independence between errors), it also becomes unclear if more AI makes the error rate better or worse.