empirical-prompt-tuning

To empirically evaluate prompt tuning, you dispatch subagents to execute target tasks, collect objective metrics like tool usage, duration, and retries, and iteratively refine prompts based on structured reports. This establishes a repeatable baseline to measure improvements.

Prompt evaluation metrics include tool usage, task duration, retry counts, and critical failure tracking. These objective data points guide iterative improvements and surface actionable refinements for multi-scenario testing.

Yes, multi-scenario testing runs various task scenarios to detect edge cases and improve robustness. Bias removal is achieved by designing prompts that minimize ambiguity and surface hidden assumptions across these different scenarios.

Yes, the empirical prompt tuning process requires the ability to dispatch subagents. It relies on subagent execution to measure tool usage, duration, retries, and track critical failures to guide iterative refinement.

The best way to establish a baseline is by running an empirical evaluation cycle on the target skill. This produces structured reports with success metrics, allowing you to compare subsequent iterations objectively.