empirical-prompt-tuning

You evaluate prompt quality without author bias by deploying automated agents to execute target prompts and gather performance metrics. This approach uses neutral evaluators to isolate instruction quality from author bias.

A structured prompt evaluation report includes success or failure status, accuracy, steps taken, duration, retries, and qualitative feedback to drive iterative improvements.

You set up an iterative prompt testing workflow by establishing a baseline, designing scenarios, and dispatching subagents to run target prompts for quantitative scoring and prompt improvements.

Prompt evaluation works without subagent dispatch by gracefully handling environments where subagent dispatch is unavailable, ensuring you still receive comprehensive reporting and qualitative feedback.

You can use automated agents for prompt revision and verification by applying this skill during prompt creation to execute target prompts, mitigate bias, and return structured assessments.