What is Evaluating AI?
Evaluating AI — The systematic assessment of an AI model’s performance, safety, and ethical alignment before deployment.
AI evaluation goes beyond accuracy metrics to assess safety, fairness, robustness, and alignment with business objectives. A model that scores 95% on benchmarks may still fail in production if it is biased, brittle to edge cases, or misaligned with user needs.
Frequently Asked Questions
What metrics should I use to evaluate AI?
Accuracy, precision, recall, and F1 for classification. BLEU/ROUGE for text generation. Human evaluation for subjective quality. Business metrics like cost savings and user satisfaction for overall value.
How do I evaluate LLM outputs?
Use a combination of automated metrics (coherence, factuality scores) and human evaluation (relevance, helpfulness ratings). LLM-as-judge approaches use one model to evaluate another.
How often should I re-evaluate models?
Continuously in production through monitoring dashboards. Formal re-evaluation should occur monthly or whenever data distributions shift, model updates are deployed, or new edge cases are discovered.