Why Standard Metrics Fail
Accuracy, F1 score, BLEU — these metrics tell you how a model performs on a static test set, not how it behaves when a user gives it malformed input at 2pm on a Tuesday. The gap between benchmark performance and production behavior is where most AI deployments fail silently.
Building Useful Evaluations
Effective evaluation pipelines test for the things that actually cause incidents: input outliers, prompt injection attempts, concept drift over time, and edge cases specific to your domain. They run continuously, not just before a model release. And they include human review loops for the cases where automated metrics give false confidence.
The Implementation Pattern
Start with a small set of high-signal evaluations rather than trying to measure everything. Track the metrics that predicted past failures. Add new evaluations when you discover a failure mode, not when a paper proposes a new benchmark. And always keep a human in the loop for anything customer-facing.