When building AI systems, two concepts often come up and sometimes get confused: RAG (Retrieval-Augmented Generation) and MCP (Model Context Protocol).
They solve different problems but usually work best together.
What RAG does
- Expands your model’s knowledge beyond training data.
- Pulls information from external sources like docs, databases, or private data.
- Great for reducing hallucinations and keeping answers grounded in facts.
Example: Asking a model about your company’s internal policies, RAG lets it fetch the right info from your docs.
What MCP does
- Structures the interaction layer of your AI.
- Defines how messages, memory, and metadata are passed.
- Makes AI systems more modular, consistent, and easier to scale.
Example: Instead of just dumping data into a prompt, MCP organizes how tools, memory, and state interact across sessions.
How they fit together
- RAG expands what your model knows.
- MCP organizes how that knowledge is delivered.
Used together, they create AI systems that are both knowledgeable and structured.
Watch our short breakdown here: RAG vs MCP Explained Super Easy!
We usually share short-form content on AI engineering and dev tools on YouTube :)
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