Reranker Production Evaluation: Guarding Retrieval Quality with Hard Negatives, NDCG@10, and Shadow Re-ranking
Rerankers often boost retrieval quality, but model upgrades can introduce domain regressions, tail latency, and candidate truncation issues. This article presents a production evaluation and release methodology—from hard negative construction and NDCG@10 offline gates to shadow re-ranking validation—to safely ship new ranking models.