KG-RAG is a task-agnostic framework that combines the explicit knowledge from a knowledge graph with the implicit knowledge of a large language model. Here, we utilize a massive biomedical knowledge graph SPOKE as the provider of biomedical context. A key feature of KG-RAG is its ability to extract 'prompt-relevant context' from the SPOKE knowledge graph, defined as the minimal context required to respond to a user prompt.