Embedding Models in Production: Selection, Versioning, and the Index Drift Problem
Your RAG answered correctly yesterday. Today it contradicts itself. Nothing obvious changed — except your embedding provider quietly shipped a model update and your index is now a Frankenstein of mixed vector spaces.
Embedding models are the unsexy foundation of every retrieval-augmented system, and they fail in ways that are uniquely hard to diagnose. Unlike a prompt change or a model parameter tweak, embedding model problems surface slowly, as silent quality degradation that your evals don't catch until users start complaining. This post covers three things: how to pick the right embedding model for your domain (MTEB scores mislead more than they help), what actually happens when you upgrade a model, and the versioning patterns that let you swap models without rebuilding from scratch.
