We applied RAG to translation across 5 LLMs and 5 EU languages. Terminology Drift errors dropped 17–45% We've been running a study on RAL (Retrieval Augmented Localization). The pattern is structurally identical to RAG: at inference time, decompose the source paragraph into n-grams, embed them, cosine similarity search against a glossary vector index, inject matched terms into the model's context, generate. Only matched terms get injected, so glossary size doesn't bloat the context window. The premise is that production localization translates tiny units in isolation - a JSON locale string, a CMS block, a CI/CD diff. Each request hits the LLM with no surrounding context, no signal that it's EU legal prose vs. marketing copy. Terminology drift is the default, and it compounds: after ten releases without a glossary, three different wrong translations of "provider" coexist in the same product.…