empirical-prompt-tuning

Empirical prompt tuning improves prompts by dispatching a subagent to execute them across predefined scenarios, collecting structured execution reports and instruction-side metrics to guide minimal, targeted patches.

Prompt evaluation collects structured artifacts including a requirements checklist, tool usage data, execution duration, and qualitative self-reports to diagnose unexpected agent behavior and drive data-driven improvements.

You diagnose unexpected agent behavior by running the revised prompt through a white-box execution agent, analyzing the generated execution reports, and applying minimal patching based on the feedback loop to converge on expected results.

Yes, empirical evaluation is designed for refining slash commands, task prompts, and other agent directives by iterating with a subagent across scenarios to improve robustness and reduce ambiguity.

The best way to reduce ambiguity is applying an iterative evaluation workflow that uses a requirements checklist and feedback loop to guide minimal, targeted prompt improvements until the agent behavior converges with expectations.

Yes, empirical prompt tuning requires a white-box execution agent to run the target prompts, generate structured evaluation artifacts, and provide the self-reports needed for bias-free, data-driven improvements.