Book: RAG Pocket Guide: Retrieval, Chunking, and Reranking Patterns for Production Also by me: Thinking in Go (2-book series) — Complete Guide to Go Programming + Hexagonal Architecture in Go My project: Hermes IDE | GitHub — an IDE for developers who ship with Claude Code and other AI coding tools Me: xgabriel.com | GitHub When the embedding API itself collapses, the moments-based detector I wrote about earlier catches it. This post is about the other failure mode: the API is fine, your data has moved, and your dashboard still says green. You ship a product launch on Monday. New SKUs, new docs, three thousand fresh chunks indexed alongside the existing eighty thousand. Nothing in the RAG pipeline changed. The embedding model is pinned. The chunker is pinned. The reranker config is byte-for-byte the same as last week. By Wednesday, the support team is forwarding screenshots. Customers asking about the old product line are getting answers that quote the new one. Top-1 hit rate on your eval set is fine.…