eval-suite-design

What problem does it solve?

This Skill helps product teams design and implement end-to-end eval suites for AI features, ensuring reliable performance and measurable quality across iterations.

Core Features & Use Cases

• Three eval types: code-based (deterministic checks), human (qualitative judgments), and LLM-as-judge (scalable qualitative scoring).
• Three-layer structure: pre-commit, pre-deploy, and production monitoring to catch issues at each stage.
• Standardized workflow: explicit steps from error analysis through design, prompting, calibration, and measurement.
• Use cases: validating AI feature safety, reliability, and user-impact tradeoffs across updates.

Quick Start

Design an eval plan by prioritizing the top failure categories identified from error analysis and draft the corresponding evals.