An LLM without RAG relies on pre-trained data, which limits its ability to handle proprietary data and may lead to hallucinations. On the other hand, an LLM with RAG improves accuracy and reliability by utilizing proprietary, non-public databases, thereby reducing the occurrence of hallucinations.