This reminded me of Gaussian Processes. You start out with n-dimensional unconstrained (but strongly correlated) gaussians. As soon as constraints (data) are added (mathematically it's called conditioning), the thing goes more and more into shape.
Prompting feels a lot like this conditioning phase to me. You start with an LLM in unconstrained mode, basically just a "soup" of knowledge. If you prompt wisely, you immediately condition the LLM into "your space of (domain) knowledge".
What comes out is an extended version of your existing knowledge.
Prompting feels a lot like this conditioning phase to me. You start with an LLM in unconstrained mode, basically just a "soup" of knowledge. If you prompt wisely, you immediately condition the LLM into "your space of (domain) knowledge".
What comes out is an extended version of your existing knowledge.