Question

A team has been improving a customer-support summarization prompt by reading a few outputs each day and tweaking wording whenever a summary 'feels off.' Recent changes seem to help some cases but silently break others, and no one can tell whether overall quality is trending up or down. Which change to their process best addresses this problem?

Accepted Answer

Establish a representative evaluation set with defined success criteria and graded metrics, then measure each prompt change against it before shipping.

Answer

Increase the model's temperature so summaries have more variety, making quality differences easier to notice by eye.

Answer

Have each team member review more sample outputs per day so subjective impressions are based on a larger informal sample.

Answer

Switch to a larger, more capable model so that manual spot-checking becomes unnecessary.