Tag: Vector Search

4 articles

• LLM Embedding Distance Metric Governance in Production: Avoiding Recall Distortion with Normalization Contracts, Metric Matrices, and Vector Replay

  In vector search, mismatches between models, normalization, and distance metrics silently distort ranking and thresholds. This article provides normalization contracts, metric matrices, golden vector replay, and migration guardrails to stabilize recall quality during model upgrades and index switches.

  07/21/2026 LLM / Embedding / Vector Search / Retrieval Engineering

• LLM Embedding Index Migration in Production: Avoiding Recall Drift with Dual-Writes, Alias Switching, and Recall Gates

  A systematic guide to zero-downtime embedding index migration covering dual-writes, alias switching, backfill validation, and recall gates to prevent recall drift during model upgrades.

  07/08/2026 LLM / Embedding / Vector Search / RAG / Production

• LLM Retrieval Index Update in Production: Using Blue-Green Index and Incremental Backfill to Prevent RAG Quality Regression

  This article explains how to govern RAG retrieval index updates, covering blue-green indexes, dual-write, incremental backfill, alias switching, quality sampling, and rollback strategies to help teams reduce retrieval quality regression risk when documents, embedding models, or chunking rules change.

  07/04/2026 LLM / RAG / Vector Search / Embedding / Production Engineering

• Contextual Retrieval in Production: Making RAG Chunks Retrievable with Context

  A practical guide to Contextual Retrieval: indexing with context, hybrid search, reranking, and deployment checks to reduce missed recall and improve answer traceability.

  06/30/2026 RAG / Contextual Retrieval / Hybrid Search / Reranking / Vector Search