The Multilingual RAG Retrieval Gap: Why Cross-Lingual Queries Silently Fail Your Vector Search
A team builds a RAG system. English retrieval hits 94% recall. They ship. Three months later, support tickets from French and German users pile up — the chatbot keeps returning irrelevant results or nothing at all. The engineers look at their monitoring dashboard. Overall recall: 91%. Nothing looks broken.
The corpus is English. The embedding model is English-only. The users are not. Every French query gets embedded into a vector space that was never designed to share coordinates with the English documents it's searching against. The cosine similarities aren't bad — they're geometrically meaningless. And because aggregate metrics aggregate, the problem is invisible until users complain loudly enough.
This is the multilingual RAG retrieval gap, and it's one of the most common silent failure modes in production AI systems serving non-English audiences.
