empirical-prompt-tuning

Agent prompt tuning uses a structured process of empirical evaluation and iterative improvement, requiring human executor feedback and system metrics to assess and continuously refine prompt quality until no further improvement is possible.

Evaluating agent behavior involves using a structured checklist across multiple scenarios, combining executor self-reports with side metrics to systematically identify and resolve deficiencies in prompt quality and effectiveness.

You need iterative prompt tuning when creating new agent prompts, modifying existing ones, or when your current agent behavior does not meet expected performance standards across multiple operational scenarios.

Yes, empirical prompt evaluation requires human executor feedback alongside system side metrics to accurately assess prompt quality and drive the iterative improvement process effectively.

The process requires continuous human executor feedback and system metrics, meaning it cannot autonomously verify improvements and relies on a manual checklist loop until iterative refinement yields no further changes.