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.
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