Judges as a Lifecycle
Most teams validate an LLM judge once, ship it, and never look at it again. Netflix runs judges over hundreds of thousands of show-level recommendation explanations per week, served to millions of members on mobile, and this writeup describes what it takes to keep one honest at that volume.
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Four phases instead of one artifact: Birth defines multiple evaluation criteria and builds curated benchmarks with human labels and rationales. Training refines the rubric. Deployment puts the judge to work. Monitoring watches for drift and triggers re-tuning behind a review gate.
Rubric tuning carries the learning signal: Reasoning-Aligned Rubric Tuning uses a meta-judge over the judge's reasoning output as the learning signal, so mismatches between judge and human get traced back to specific rubric language rather than patched with more prompt text.
One judge, two roles: The same judge gates quality and drives reflective generation, appending its rationale to the generator prompt so failed explanations get revised instead of dropped.
Why it matters: A five-week A/B test over tens of millions of members shifted viewing toward previously unwatched content and increased successful browse-to-play sessions against a no-explanation control, with no quality-related takedowns. This is the rare LLM-judge writeup with production consequences attached.