Your RAG system didn't hallucinate. It retrieved an outdated document with a 0.94 similarity score and answered with total confidence.

This episode breaks down the RAG Freshness Trap: why high vector similarity scores frequently mask stale context, how embedding drift happens quietly in enterprise vector stores, why standard monitoring dashboards show green while your model lies to customers, and the exact hybrid retrieval pattern needed to enforce metadata governance.

If your retrieval layer isn't checking document timestamps at runtime, your AI is making decisions on last quarter's rules.

Keywords: RAG architecture, vector database, cosine similarity, embedding drift, RAG freshness, hybrid retrieval, enterprise AI, AI observability, LLM context, metadata filtering, AI governance, vector search

This is Maya. New episodes three times a week.youtube.com/@mayabuildsai