Why Accuracy Is Not Enough: Evaluation Metrics Every AI Engineer Should Understand
Your evaluation dashboard says your model is 95% accurate. Leadership is happy. The deployment goes live.
Two weeks later, users complain that critical failures are still slipping through.
The problem is not always the model. Sometimes the problem is the metric.
As AI systems move from research prototypes into production infrastructure, evaluation becomes one of the most important engineering problems. This is especially true for modern GenAI systems, where outputs are probabilistic, subjective, and highly context dependent.
