What problem does it solve?
Prompt quality is hard to gauge from static reading; empirical-prompt-tuning provides a repeatable, bias-free workflow that runs prompts in a blank-slate executor and evaluates results from both the executor and the instruction side. It helps teams converge on robust prompts by surfacing unclear points and applying minimal, principled diffs.
Core Features & Use Cases
- Bias-free evaluation: run prompts with a neutral executor to reveal hidden ambiguities.
- Two-sided evaluation: capture executor results and instruction-side metrics for balanced feedback.
- Iterative refinement: define evaluation baselines, scenarios, and a ledger of failure patterns; apply minimal diffs per iteration.
- Structured reporting: trace the reasoning, collect unclear points, and track progress with convergence criteria.
- Practical use: for high-stakes prompts and frequently used skills, ensure robust performance before production deployment.
Quick Start
Run an Iteration 0 consistency check between the description and the body, then prepare baseline evaluation scenarios and a requirements checklist.
