AI Evaluation
AI evaluation is the practice of systematically measuring how well an AI system performs against defined criteria—accuracy, latency, cost, safety, user satisfaction—before and after deployment. This sounds obvious. It is not widely practiced. Most organizations deploying AI in 2025 evaluate by vibes: someone runs a few test queries, the results look reasonable, and the system ships. Rigorous evaluation requires test datasets that represent real usage, metrics that map to business outcomes (not just model benchmarks), and automated pipelines that run evaluations on every change. The gap between "works in a demo" and "works reliably in production" is almost entirely an evaluation gap. Without good evals, you cannot tell whether a prompt change, model upgrade, or architecture tweak made things better or worse. You are flying blind and calling it agile.
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