Benchmarks are not business metrics
An LLM that scores 95% on a benchmark may still fail catastrophically in production. Benchmarks measure model capability in controlled conditions. Production measures system reliability under real-world chaos.
The four evaluations that matter
1. Task success rate — Did the system complete the task correctly, end-to-end?
2. Escalation quality — When the system was uncertain, did it escalate appropriately?
3. Cost per decision — What does it cost in compute, latency, and human review to produce one correct decision?
4. Failure mode analysis — When the system is wrong, how is it wrong? Confidently wrong, or uncertain and wrong?
Evaluation as a continuous system
One-time evaluation before launch is necessary but insufficient. Production AI systems need continuous evaluation: drift detection, edge case collection, A/B testing, and human feedback loops.
Evaluate your AI system the way your most skeptical stakeholder would: not by what it gets right on average, but by what happens when it gets something wrong.