I’m familiar with how those are used, but not sure what you mean in this context.

Have another model (or even another instance of the same model) review the output of the first.

Models will hallucinate. They are also quite good at spotting hallucinations in other models' output (with some more hallucinations thrown in). With a threshold for confirmation, and a few iteration loops, you arrive at a fixed point where every claim is supported.