LLMs lack context, and I found the more information I provided the better. At some point it was better to just talk to the LLM like I would anyone else. For that matter, LLMs were trained on human speech anyway. It isn't like it was trained on if-else blocks like an Alexa speaker that tries to string together recognized tokens into a pre-configured execution flow.

And finally, LLMs also lack the emotional or human context for why I am doing the specific thing I am doing. Otherwise it will revert to the mode/mean in everything it does. This is obvious, btw: LLMs are generative but they are trained on and largely produce median results if given median inputs. To get results that are "outside the mean/median/average/mode", you need to provide it sufficient context, tokens and input to guide it towards a path that generates higher quality output.

Once you stop approaching LLMs like a machine, and view them more like pseudo-random walks across the compressed set of human written knowledge, it is a little clearer (or at least was to me) how to better write to them.