> The model might have uncountable ways to reply to that question if you had inserted more tokens, but with only the question in context, you'll always get answ

> The model might have uncountable ways to reply to that question if you had inserted more tokens, but with only the question in context, you'll always get answers that are clustered around the mean answer it can produce... but because it's the literal mean of all those possibilities it's unlikely user A or user B will find particularly great. I refer to it as giving the LLM "pedagogical context" since a core part of teaching is predicting what kind of answer will actually help the audience depending on surrounding context. The question "What is multiplication?" demands a vastly different answer in an elementary school than a university set theory class. I think that's why there's such a large variance in HNer's experience with ChatGPT. The GPT API with a custom system prompt is far more powerful than the ChatGPT interface specifically because it grounds the conversation in the way that the moderated ChatGPT system prompt can't. The chat GUI I created for my own use has a ton of different roles that I choose based on what I'm asking. For example, when discussing cuisine I have roles like (shortened and simplified) "Julia Childs talking to a layman who cares about classic technique", "expert molecular gastronomy chef teaching a culinary school student", etc.