If you've lately been involved in AI development, you've most likely come across two acronyms that appear frequently: RAG and MCP. They're regularly stated in the same sentence, sometimes seen as rivals, and frequently misinterpreted. To summarise, Retrieval-Augmented Generation (RAG) and the Model Context Protocol (MCP) address two distinct difficulties. RAG enables an AI model to learn more. MCP enables an AI model to perform more. RAG and MCP work together to create agentic AI systems that obtain actual information, do real-world activities, and intelligently answer questions. As corporations want to build generative AI in order to remain competitive, knowing the differences between RAG and MCP and how they interact is becoming critical information not just for engineers but also for anybody making AI strategy choices. In this tutorial, we'll describe what RAG and MCP are, how they operate, when to utilise them, and where the ecosystem is going by 2026.
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