Title for the back of the class:

"Prompts sometimes return null"

I would be very cautious to attribute any of this to black box LLM weight matrices. Models like GPT and Opus are more than just a single model. These products rake your prompt over the coals a few times before responding now. Telling the model to return "nothing" is very likely to perform to expectation with these extra layers.

Out of curiosity, are there any sources to there being a significant amount of other steps before being fed into the weights

Security guards / ... are the obvious ones, but do you mean they have branching early on to shortcut certain prompts?

Thanks, I was already distracted after the first sentence, hoping there would be a good explanation.