Two terms you'll keep hearing around modern AI assistants — RAG and MCP. Beginners often assume they're two versions of the same thing. They're not.
The short version: RAG helps AI find information. MCP helps AI use tools.
Think of AI as a new hire at your company. They're smart, but hit two walls immediately:
They don't know your documents (HR policy, product manuals, customer records) — that's the gap RAG fills, by searching your actual current data before answering instead of guessing from stale training data.
Even once they know the answer, they can't send an email or query a database on their own — that's the gap MCP fills, giving AI a standard way to call real tools (Gmail, a database, GitHub, a calendar) instead of a custom integration per app.
RAG's pipeline: your question → an embedding model turns it into a vector → a vector database (Pinecone, Weaviate, pgvector) finds the closest-matching documents → the language model reads them and answers.
MCP's pipeline: the AI client → an MCP client → an MCP server exposing a tool (filesystem, database, calendar) → the real software doing the work, with the result routed back.
The best AI systems use both — RAG to know things, MCP to do things.
I wrote up the full version with architecture diagrams for each: MCP vs RAG Explained
Top comments (0)