> when the model won't actually be able to provide one

This is key. In my experience, asking an LLM why it did something is usually pointless. In a subsequent round, it generally can't meaningfully introspect on its prior internal state, so it's just referring to the session transcript and extrapolating a plausible sounding answer based on its training data of how LLMs typically work.

That doesn't necessarily mean the reply is wrong because, as usual, a statistically plausible sounding answer sometimes also happens to be correct, but it has no fundamental truth value. I've gotten equally plausible answers just pasting the same session transcript into another LLM and asking why it did that.