Comment by theendisney
9 hours ago
At work i only had early copilot which was hysterically bad at everything. As i wanted it to do the same task repeatedly and could spot wrong results instantly i kept evolving a prompt that attempted to correct all ways it found to do it wrong. It kept inventing new ways to get it wrong until it eventually got it right 90% of the time. My theory is that an avanced model that has no issues with a task could do the prompt enginering much better than i ever could. You could for example run x different queries that all do the same thing and compare the results y times. If there are >1 correct results and the wrong versions are all unique you should be able to drill down to a valid result with even a truly shit model running on a potato. Basically what humans do.
Yes! As long as you have some criteria to judge the final answer, you can do a kind of "prompt-side RLVR", where you have the model generate prompt changes, try a bunch of different prompts and see which ones improve the results.
You don't necessarily need a bigger model to do this.