Tag: reranker

3 articles

• 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.

  08/30/2026 reranker / information-retrieval / search-quality / ndcg / production-ml / shadow-re-ranking

• LLM Reranker Production Evaluation: Guarding RAG Retrieval Quality with Golden Query Sets, nDCG, and Latency Budgets

  A deep dive into production evaluation methodologies for LLM Rerankers in RAG systems, covering golden query set construction, nDCG and MRR ranking metrics, candidate set budget optimization, latency and cost governance, canary release gates, and rollback strategies to prevent retrieval ranking degradation.

  07/07/2026 LLM / RAG / Reranker / Retrieval Evaluation / Production AI

• Embedding & Reranker Production Evaluation: Preventing Retrieval Regression with Offline Replay and Shadow Traffic

  A systematic guide to evaluating retrieval quality after deploying Embedding and Reranker models. Covers offline replay, shadow traffic, hybrid search, metric design, and regression handling to prevent silent quality degradation.

  07/02/2026 Embedding / Reranker / RAG / Hybrid Search / Retrieval Evaluation / Shadow Traffic / Offline Replay