To evaluate a candidate draft, the Ranker injects it into a competitive set of snippets or full pages and runs multiple parallel inference samples to account for model variance. It then calculates the median rank of the target across all passes, selecting the sample closest to that median as the canonical ranking while capturing per-item rationales that explain why specific features outranked the competition.