zlacker

Are there any risks I miss to asking a model (in a separate context as to not muddy the waters) to rewrite the informal prompt into something more proper and then use that as a prompt?

Seems like a pretty simple task for an LLM as long as the initial prompt isn't too ambiguous. If it really does help with the recall it could be interesting to have this as an optional preprocessing layer in chat clients and such.

>>miven+(OP)
I do this all the time. "Summarize in a YAML like markup that retains all information." Then plug that as is into something else.

>>miven+(OP)
That is a pretty good use case. In fact, if your prompt is very long, you will need to summarize it (with an LLM).

Also, when you fine-tune the LLM, you can also use an LLM to summarize or concatenate content that you train it on (e.g. rewrite this content in the style of a human having a conversation with a computer)

>>miven+(OP)
Preprocessing prompts is actually a great approach.

Personally I think given the model loss with fine tuning people who want the cutting edge LLM at any cost would - instead of fine tuning the model itself - fine tune a preprocess prompter that takes a chat/instruction and converts it to a good TextCompletion prompt.

So for example taking "write me a paragraph of marketing copy for an athletic shoe" and tuning it into:

"Marketing case study: Athletic shoe The problem: The client needed a paragraph of high quality marketing copy to promote their new athletic shoe on their website. The solution: Our award winning copywriters wrote the outstanding copy reproduced below."

Followed by an extractor that reformats the completion result into an answer for the initial prompt, as well as potentially a safety filter that checks the result isn't breaking any rules (which will as a bonus be much more resistant to jailbreaking attempts).