[mbb70]

This always seemed like the end game vs. getting a degree in prompt engineering.

If you get enough data on "initial prompt attempt" -> "final successful prompt", the whole thing can be replaced by a fine tuned model.

You would just select a "prompt rewritter llm" that optimizes for accuracy, cost, alignment etc.

[pseudosavant]

GPT on top of GPT. It is turtles all the way down.

[passion__desire]

Let's say a particular layperson wants to execute a task. He gives (INPUT <=> OUTPUT) pairs. chatgpt creates a "prompt ( == bytecode)" which captures the essence of those transformations This process is called "Program Fitting" similar to Line fitting or Curve fitting given list of data points. Then this bytecode can then be efficiently run on a smaller distilled CVM (chatgpt virtual machine) diligently chosen by ChatGPT itself since it knows which CVM to best execute the task and then run the (bytecode = prompt) on new similar data. No need to run full ChatGPT. ChatGPT creates its own MoE setups.

[samus]

For all we know, ChatGPT 4 might function like that