You train it, and under the hood you can’t actually read out the logic tree of why each word was chosen.

Of course you can, you can look at every single activation and weight in the network. It’s tremendously hard to predict what the model will do, but once you have an output it’s quite easy to see how it came to be. How could it be bloody otherwise you calculated all that stuff to get the output, the only thing you have to do is to prune off the non-activated pathways. That kind of asymmetry is in the nature of all non-linear systems, a very similar thing applies to double pendulums: Once you observed it moving in a certain way it’s easy to say “oh yes the initial conditions must have looked like this”.

What’s quite a bit harder to do for the likes of ChatGPT compared to double pendulums is to see where they possibly can swing. That’s due to LLMs having a fuckton more degrees of freedom than two.