Yup - well you feed in the prompt along with an answer and you get the model to produce outputs and check for discrepancies. If the answer is wrong then the mod

Yup - well you feed in the prompt along with an answer and you get the model to produce outputs and check for discrepancies. If the answer is wrong then the model adjusts -- this is the way backpropagation works....I think there are huge advantages in using logic languages in order to represent some of these data sets rather than simple English or the current chain-of-thought reasoning approaches -- backpropagation as an example isn't really used in the human brain, but it leads to great results in mimicking how neural networks 'learn' - in the same way, we don't have to have the full formal picture of how humans model the logical world in order to achieve great results. We can simulate this using logic programming or even general programming or at least that's my conjecture.