I would note that while I am generally bad at it, you have to craft what you say to humans to get the response you want as well. I think the thing is that natural language can be extraordinarily more expressive than a formalistic method for abstract concepts. The prompt engineering I’ve seen are basically natural language instructions that are precise and cover many edges to constrain and provide sufficient context, but would be really difficult to encode in a formal language because they’re still very abstract concepts. They read similar to what you would tell a person if you wanted them to, say, behave like a Linux shell without having them ask any clarifying questions or leaving too much ambiguity about what you meant. Expressing what “behave like a Linux shell” means in a formal method would be very hard because there’s an awful lot that goes into those concepts. Additionally chatgpt is seeded with an originating prompt that sets the tone and behavior of the responses. A lot of “prompt engineering” is dampening the original instructions from the context for subsequent response. In the example of a Linux shell, you don’t want it explaining everything and apologizing all the time and what not - a Linux shell takes commands and outputs results in a terminal, it doesn’t apologize that it’s a large language model and not really a Linux shell - and that behavior originates from the original prompt that’s opaque to the user of ChatGPT. If you engineer the prompt right it’ll stop apologizing and just print what it computes as the best output for a Linux command in the format it expects is best representative of a terminal.