  Large language models (LLMs) trained on a substantial corpus of human
knowledge and literature productively work with a large array of facts from
that corpus. Surprisingly, they are also able to re-create the behaviors of
personae that are captured within the corpus. By forming teams of simulated
personae, supplying contexts that set the stage, and providing gentle prompts,
one can move through scenarios that elicit expert behavior to perform
meaningful cognitive work. The power of this strategy is demonstrated with two
examples, one attacking factuality of LLM responses and the other reproducing a
very recently published result in quantum optics.
