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AI Evaluation and Testing Skills
AI evaluation measures whether a system performs acceptably for its intended use and foreseeable misuse. Skills include test design, benchmark selection, dataset construction, rubric writing, statistical analysis, human evaluation, subgroup testing, robustness testing, red teaming, reproducibility, and failure analysis.
Applied evaluators state what a metric measures and what it cannot establish. They connect thresholds to user value and risk. Prove competence with a reproducible evaluation pack containing scenarios, expected behavior, scoring rubric, results, failure taxonomy, subgroup or edge-case analysis, limitations, and release recommendation.
Related: AI Evaluation Specialist, AI Model Validator, Responsible AI Skills, Evidence Documentation.
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