My novice understanding of prompt-engineering is that it "guides" the LLM to certain parts of the output space. It has all this information related to a topic f

My novice understanding of prompt-engineering is that it "guides" the LLM to certain parts of the output space. It has all this information related to a topic from a variety of sources, by creating some context (i.e. prompting) one can increase the likelihood of the LLM outputting text that's more related to that part of the training data that you'd want. For example, let's say the LLM learnt about color theory from: 1. academic articles, 2. artistic discussion forums, 3. reddit ( :) ). If you wanted to steer the output from a certain "niche" you could create context in the prompt that makes the LLM more likely to create output (ie a continuation of the prompt, sort of). Tricks I've seen is stuff like "You're a super-smart, best in your field XYZ, tell me something about...". But I bet you could similar good output by using lingo and vocab specific to either niche to "prime" the LLM. Take with lots of salt-grains, just my pet-theory.