empirical-prompt-tuning

Iterative prompt refinement improves agent-facing instructions through a structured workflow involving bias-free executor evaluation, two-sided metrics, and continuous fixes. You provide the prompt, scenarios, and a checklist to evaluate clarity and effectiveness systematically.

Bias-free evaluation in prompt tuning uses a separate executor to assess instruction clarity without the original author's bias. It combines executor self-reports with objective instruction-side metrics to identify unclear points and propose targeted fixes.

Yes, iterative prompt refinement applies to slash commands, CLAUDE.md files, skills, and code-gen prompts. It evaluates any agent-facing instructions requiring high clarity for automated tasks by using scenarios and checklists to measure effectiveness.

The best way to evaluate code-gen prompts is combining two-sided self-reports with objective instruction-side metrics. A separate executor tests the instructions against defined scenarios and a checklist to identify gaps and drive iterative refinement.

No specific dependencies are required to perform two-sided instruction evaluation. The workflow operates independently using scripts and references to assess agent-facing instructions through executor self-reports and objective metrics.

AI agents follow instructions inconsistently when prompts lack clarity. Iterative prompt tuning addresses this by using a bias-free executor to test scenarios, measuring instruction-side metrics, and proposing structured fixes to eliminate ambiguity.