> ## Documentation Index
> Fetch the complete documentation index at: https://handbook.sutro.sh/llms.txt
> Use this file to discover all available pages before exploring further.

# Eval Approaches

> Common AI eval approaches and the role each one plays in measurement.

Most eval systems combine multiple approaches:

## Static Evals

* **Static eval sets:** curated examples that are run repeatedly to catch regressions and compare candidate changes.
* **[LLM judges](/primitives/judges):** model-based evaluators that map unstructured outputs into bounded labels, rationales, or classifications.
* **[Human annotation](/patterns/context/expert-annotation):** expert review used to ground judge behavior, audit model performance, and build trust in measurements.
* **Production sampling:** real traces or records sampled from live usage to discover new failure modes and measure field behavior.
* **[Operational metrics](/deployment):** latency, cost, refusal rate, escalation rate, completion rate, and other system-level signals.

No single eval method is sufficient on its own. Static evals provide repeatability, judges provide scale over unbounded behavior, and human review provides grounding.