Eval Designer

Overview

This skill covers end-to-end design of evaluation frameworks for LLM-powered systems. It helps teams define what "good" looks like for their specific use case, create diverse test suites that cover both capability and failure modes, design human evaluation rubrics with clear scoring criteria, implement automated eval pipelines using reference-based and LLM-as-judge approaches, and track quality over time as models and prompts change. A robust eval framework is the engineering foundation that enables confident model upgrades, prompt changes, and feature launches.

When to Use

• Building an eval suite before deploying an LLM-powered feature for the first time
• Designing automated evals to run in CI/CD pipelines for prompt or model changes
• Creating human evaluation rubrics with scoring guidelines for labeler studies
• Defining safety evals to test for harmful outputs, jailbreaks, or policy violations
• Measuring quality regression after a model upgrade (e.g., GPT-4 → GPT-4o)
• Setting up LLM-as-a-judge evaluation for tasks without clear ground truth
• Establishing baseline metrics before A/B testing different prompts or models
• Auditing an existing eval suite for coverage gaps or measurement validity

When NOT to Use

• Training or fine-tuning models (use model training skills)
• Collecting and curating datasets for training (use dataset-curator skill)
• Comparing publicly available model benchmarks like MMLU or HumanEval (use model-comparator skill)