You’ve updated your system prompt to fix a specific bug. How do you ensure this "fix" didn't break 10 other things the model was previously doing correctly?

How do you calculate the ROI of a prompt change? If a new prompt is 5% more accurate but 50% more expensive in tokens, how do you decide if it’s worth it?

How do you measure if the LLM actually answered the user’s question, even if the facts it provided were technically true?

Why is it harder to A/B test an LLM prompt than a UI button color? How do you account for the "non-deterministic" nature during the test?