(Title edited, was slightly too long)

LLM technology specifically beam-searches manifolds (or latent space) of lingustics that are closely related to the original prompt (and the pre-prompting rules of the chatbot) which it then limits its reasoning inside of. Its just the basic outcome of weights being the primary function of how it generates reasonable answers.

This is the core problem with LLM tech that several researchers have been trying to figure out with things like 'teleportation' and 'tunneling' aka searching related, but lingusitically distant manifolds

So when you pre-prompt a bot to be friendly, it limits its manifold on many dimensions to friedly linguistics, then reasons inside of that space, which may eliminate the "this is incorrect" manifold answer.

Reasoning is difficult and frankly I see this as a sort of human problem too (our cognative windows are limited to our langauge and even spaces inside them).