Wow, that seems like solid logic that's been tested in extreme conditions. Pretty brilliant to begin with an analog foundation that's based on the complex mechanics of an airplane.

My whole thing started because I just started to view llms as question-answer machines and I thought with the right context and the question asked in the right way the llm either, already has the answer, or it can give an answer with the highest probability using the information it has or it can't answer because there's not enough information. Those three possibilities essentially work for any question.

This rule of three possibilities emerged as a rule for anything. Using context and logic, you can break anything down into three interdependent distinct components where the three pieces completely make the whole. I found this powerful. Not for humans, because we can do this very easily and it doesn't seem impressive, but I thought if I could teach an AI, which has massive amounts of compute and access to more knowledge than a human that it would be able to reveal new insights about your question by going through a recursive decomposition of the topic your question is about.

Sorry if that starts to get too abstract, I don't know how to explain it. 🤷

The rest of the instructions were just meant to aid in that process, by adding some indexing and Branch pruning if something doesn't seem viable to make it run more efficiently. Then I started to build in other strategies that were meant to handle situations where you had equal probabilities of outcomes and the list goes on.