Evaluating AI

Executive Definition: Evaluating AI — The systematic assessment of an artificial intelligence model’s performance, safety, robustness, and alignment before and after deployment.

Evaluating AI goes beyond standard accuracy metrics to assess critical factors such as fairness, security, and alignment with intended use cases. A model might score highly on academic benchmarks, but still fail in real-world production if it is biased, brittle to unexpected inputs, or poorly aligned with user needs.

Frequently Asked Questions

What metrics should I use to evaluate AI?

It depends on the task. Use accuracy, precision, recall, and F1-score for classification. For generative tasks, use automated metrics like perplexity alongside human evaluation and business metrics like task completion rate.

How do I evaluate Large Language Model (LLM) outputs?

Modern approaches combine human evaluation (rating relevance and helpfulness) with ‘LLM-as-a-judge’ methods, where a capable model evaluates the coherence, factuality, and safety of another model’s outputs.

How often should I re-evaluate models?

Evaluation should be continuous in production through monitoring dashboards. Comprehensive re-evaluation is necessary whenever the data distribution shifts, the model is updated, or new edge cases are discovered.