One misconception about RAG is that people think it's a knowledge system. It's actually a retrieval system. The quality of answers depends far more on the quality of your knowledge architecture than on the LLM.

The hardest enterprise problems usually aren't embeddings or vector databases they're deciding which document is authoritative, when it's valid, who owns it, and when it should be ignored. If retrieval surfaces obsolete or conflicting information, the model simply makes that confusion easier to consume.

The organizations that get the most value from RAG treat it as the final layer on top of strong governance, versioning, and content lifecycle management—not as a replacement for them. AI can retrieve knowledge, but it can't manufacture organizational truth.