Practice · Established · Intermediate

Model Evaluation

Measuring whether a model actually works, on data it has never seen, using metrics matched to the decision it supports.

What Model Evaluation is

Evaluation is a design problem before it is a measurement problem: what counts as success, which errors cost most, and which population the model must work for.

How it works

Teams hold out representative test sets, report metrics with confidence intervals, slice results by segment to catch hidden failures, and compare against a naive baseline before celebrating.

Why it matters

Weak evaluation is why so many models pass review and then disappoint. For generative systems, where there is no single right answer, it is the hardest open problem in shipping.

Common uses

→Pre-launch model review
→A/B testing new versions
→Regression testing after fine-tuning
→LLM evaluation suites

Strengths

✓Catches problems before users do

Watch for

✓Aggregate metrics hide subgroup failures
✓Benchmarks get gamed and contaminated

Continue exploring

LLM Evaluation A/B Testing for AI Bias in AI