RAG and MCP solve different problems in production AI systems: RAG grounds models in static knowledge via semantic search, while MCP enables live system interactions like API calls and workflow triggers. They are complementary, not competing. Production teams increasingly combine both in sequential or hybrid architectures. Key
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How to decide between RAG, MCP, or bothSecurity risks when LLMs can take actionsWhy MCP uses more tokens, and how to fix itBuilding a hybrid RAG and MCP architectureYour next step: Production-grade AIFrequently asked questionsShip Faster with AISort: