I think that chain-of-thought for LLMs is just helping them enhance their "memory", as it puts their reasoning into the context and helps them refer to it more readily. That's just a guess, though.
That’s pretty much correct. An LLM is often used rather like a forecast model that can forecast the next word in a sequence of words. When it’s generating output it’s just continuously forecasting (predicting) the next word of output. Your prompt is just providing the model with input data to start forecasting from. The prior output itself also becomes part of the context to forecast from. The output of “think about it step-by-step” becomes part of its own context to continue forecasting from, hence guides its output. I know that “forecasting” is technically not the right term, but I’ve found it helpful to understand what it is LLM‘s are actually doing when generating output.
