The "fact database" is the old AI solution, e.g. Cycorp; it doesn't quite work either. Knowing what is true is a really hard, unsolved problem in philosophy, see e.g. https://en.wikipedia.org/wiki/Gettier_problem . The secret to modern AI is just to skip that and replace unsolvable epistemology with "LGTM", then sell it to investors.

There are some things that we can define as "definitely true as close as makes no difference" in the context of an LLM:

- dictionary definitions - stable apis for specific versions of software - mathematical proofs - anything else that is true by definition rather than evidence-based

(i realize that some of these are not actually as stable over time as they might seem, but they ought to do good enough with the pace that we train new models at).

If you even just had an MOE component whose only job was verifying validity against this dataset in chain-of-thought I bet you'd get some mileage out of it.