For that you need a program to judge the quality of output given some input. If we had that, LLMs could just improve themselves directly, bypassing any need for prompt engineering in the first place.
The reason prompt engineering is a thing is that people know what is expected and desired output and what isn't, and can adapt their interactions with the tool accordingly, a trait uniquely associated with adaptive complex systems.
can adapt their interactions with the tool accordingly
If we could have programmed around this prior, then people who can and can't Google wouldn't be a thing: Google would just know what results to bring up without the search-curse-refine-repeat cycle. Prompt engineering seems like an extension of Google search-fu.
If we had that, LLMs could just improve themselves directly, bypassing any need for prompt engineering in the first place.
Prompt tuning is not the only way to fine tune the output of an LLM, and since the goal for most is going to be to make them usable by anyone, that's going to be the least desirable route.
I know LLMs are used to grade LLMs. That isn't solving the problem, it's just better than nothing because there are no alternatives. There aren't enough humans willing to endlessly sit and grade LLM responses.
Yes there are, in addition to the thumbs up/down buttons that most people don't use, you can also score based on metrics like "did the person try to rephrase the same question again?" (indication of a bad response), etc. from data gathered during actual use (which ChatGPT does use for training).
Firstly, I'm willing to bet only a minority of users regularly use those buttons. Secondly, you're talking about the most popular LLM(s) out there. What about all the other LLMs almost nobody is using but are still being developed/researched? Where do they find humans willing to sit and rate all the garbage their LLM puts out?
For that you need a program to judge the quality of output given some input. If we had that, LLMs could just improve themselves directly, bypassing any need for prompt engineering in the first place.
The reason prompt engineering is a thing is that people know what is expected and desired output and what isn't, and can adapt their interactions with the tool accordingly, a trait uniquely associated with adaptive complex systems.
If we could have programmed around this prior, then people who can and can't Google wouldn't be a thing: Google would just know what results to bring up without the search-curse-refine-repeat cycle. Prompt engineering seems like an extension of Google search-fu.
Yep, exactly, and it's been studied and put in to practice effectively already.
Prompt tuning is not the only way to fine tune the output of an LLM, and since the goal for most is going to be to make them usable by anyone, that's going to be the least desirable route.
I know LLMs are used to grade LLMs. That isn't solving the problem, it's just better than nothing because there are no alternatives. There aren't enough humans willing to endlessly sit and grade LLM responses.
Yes there are, in addition to the thumbs up/down buttons that most people don't use, you can also score based on metrics like "did the person try to rephrase the same question again?" (indication of a bad response), etc. from data gathered during actual use (which ChatGPT does use for training).
Firstly, I'm willing to bet only a minority of users regularly use those buttons. Secondly, you're talking about the most popular LLM(s) out there. What about all the other LLMs almost nobody is using but are still being developed/researched? Where do they find humans willing to sit and rate all the garbage their LLM puts out?