How do you evaluate LLM output quality at scale without human labeling?

At scale, LLM evaluation uses automated metrics, synthetic benchmarks, and LLM-as-a-judge systems instead of human labeling.

Human evaluation is expensive and slow, so production systems rely on proxy metrics. These include embedding similarity, factual consistency checks, rule-based validators, and LLM-as-a-judge scoring systems. Synthetic datasets are also used to continuously benchmark model behavior in regression tests.