Comment by 6Az4Mj4D 10 months ago As I was reading that prompt, it looked like large blob of if else case statements 3 comments 6Az4Mj4D Reply refactor_master 10 months ago Maybe we can train a simpler model to come up with the correct if/else-statements for the prompt. Like a tug boat. otabdeveloper4 10 months ago Hobbyists (random dudes who use LLM models to roleplay locally) have already figured out how to "soft-prompt".This is when you use ML to optimize an embedding vector to serve as your system prompt instead of guessing and writing it out by hand like a caveman.Don't know why the big cloud LLM providers don't do this. MaxLeiter 10 months ago This is generally how prompt engineering works1. Start with a prompt2. Find some issues3. Prompt against those issues*4. Condense into a new prompt5. Go back to (1)* ideally add some evals too
refactor_master 10 months ago Maybe we can train a simpler model to come up with the correct if/else-statements for the prompt. Like a tug boat. otabdeveloper4 10 months ago Hobbyists (random dudes who use LLM models to roleplay locally) have already figured out how to "soft-prompt".This is when you use ML to optimize an embedding vector to serve as your system prompt instead of guessing and writing it out by hand like a caveman.Don't know why the big cloud LLM providers don't do this.
otabdeveloper4 10 months ago Hobbyists (random dudes who use LLM models to roleplay locally) have already figured out how to "soft-prompt".This is when you use ML to optimize an embedding vector to serve as your system prompt instead of guessing and writing it out by hand like a caveman.Don't know why the big cloud LLM providers don't do this.
MaxLeiter 10 months ago This is generally how prompt engineering works1. Start with a prompt2. Find some issues3. Prompt against those issues*4. Condense into a new prompt5. Go back to (1)* ideally add some evals too
Maybe we can train a simpler model to come up with the correct if/else-statements for the prompt. Like a tug boat.
Hobbyists (random dudes who use LLM models to roleplay locally) have already figured out how to "soft-prompt".
This is when you use ML to optimize an embedding vector to serve as your system prompt instead of guessing and writing it out by hand like a caveman.
Don't know why the big cloud LLM providers don't do this.
This is generally how prompt engineering works
1. Start with a prompt
2. Find some issues
3. Prompt against those issues*
4. Condense into a new prompt
5. Go back to (1)
* ideally add some evals too