RAG vs MCP: What’s the Difference?

One expands knowledge, the other organizes it. Here’s how they work together.

Hi, (firstname)!

When building AI systems, it’s easy to confuse frameworks like RAG and MCP. They sound similar but solve very different problems. Let’s break it down.

🔹 RAG (Retrieval-Augmented Generation)
Think of RAG as your model’s memory upgrade. Instead of relying only on what it was trained on, it can retrieve external knowledge, from your documents, databases, or private sources. The right chunk of data gets injected into the prompt, so the response is grounded in real, current information.

🔹 MCP (Model Context Protocol)
If RAG decides what to say, MCP decides how to say it. MCP standardizes how information flows into the model, user messages, system instructions, memory, metadata. Everything is modular and consistent, so your AI responses aren’t just smart, but well-structured.

👉 How they work together: RAG expands what the model knows. MCP organizes and delivers that knowledge effectively. Most production apps end up using both.

Want to see how we combine them in real-world systems? Watch the next video for more details: