Two teams build RAG systems for the same company using the same documents and the same LLM. One system answers questions accurately 90% of the time. The other barely hits 60%. The difference? Embedding quality. The first team used a high-quality embedding model matched to their domain and carefully evaluated retrieval precision. The second used a generic model and never measured retrieval quality. In RAG systems, if retrieval fails, nothing downstream can save you. The LLM will either hallucinate or say "I don't know." Embedding quality is the invisible foundation that determines whether your AI system actually works. Yet most teams never measure it directly.