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Comment by jzombie | Hacker News Reader

Curious as to why some sort of model specifically trained for prompt engineering couldn't be placed before a LLM in a pipeline, or used in a transfer learning scenario.

I'm also curious as to why existing LLMs can't be fine tuned to handle this, if prompt engineering is really a major concern.

Usually, if I don't get the answer I'm looking for from ChatGPT, I tell it it's not the answer I'm looking for, what the original answer was missing, and I usually get a better answer the second time around.

If it goes beyond that I sometimes resort to just cussing at it, and that usually does the trick.

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> Usually, if I don't get the answer I'm looking for from ChatGPT, I tell it it's not the answer I'm looking for, what the original answer was missing, and I usually get a better answer the second time around.

This is an expected behavior, as by default even with RLHF ChatGPT will output statistically "average" content. It's also the reason why Chain of Thoughts prompting works very effectively.

I have a (somewhat out of date) notebook demonstrating this, plus a function calling trick which allows you to get the improved result in a single API call: https://github.com/minimaxir/simpleaichat/blob/main/examples...

Thanks for sharing.

I don't think anyone says it can't. Isn't that basically what ChatGPT does to interact with DALL-E?

I don't personally know.

I've been curious if DALL-E has been truly been mixed in w/ ChatGPT as a single model using a Mixture of Experts (MoE)- type learning gate to train them all together.

> Curious as to why some sort of model specifically trained for prompt engineering couldn't be placed before a LLM in a pipeline, or used in a transfer learning scenario.

They can.

It's just important enough for performance to be worth doing this manually.

> I'm also curious as to why existing LLMs can't be fine tuned to handle this, if prompt engineering is really a major concern.

It depends what you want them to do. They're general purpose things and there isn't a one size fits all solution here.

In my custom instructions. I ask it to always reword my question better and then answer that instead. It usually works quite well for me.

As one prompt, or as two? As in you query for "reword this prompt: [...blah...]" and then manually copy-paste the answer back in, or query for "[...blah...] // instead of processing the prompt before the //, reword the prompt to make it more effective and do inference on the reworded prompt"?

I guess in the 2nd case it would be useful to ask it to output the reworded prompt too.

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