RAG & MCP Fundamentals – A Hands-On Crash Course

This practical crash course teaches you to build integrated AI systems rather than standalone tools. You will first master retrieval augmented generation or RAG to connect models to your own data for accurate contextaware answers. Next, you will learn the model context protocol MCP to coordinate communication and actions across multiple software components. By the end, you will know how to use Rag for knowledge and MCP for system level coordination to create sophisticated multi-part applications. Everyone's talking about Rag. If you feel left out, this is the only video you need to watch to catch up. In this video, we'll learn Rag in a super simplified manner with visualizations that will make it easy for anyone to understand. No background knowledge in AI or AI models or coding or programming required. We'll start with the simplest explanation of rag there is. Then we'll look into when to and when not to rag. We'll then look into what is rag. We'll then understand some of the prerequisites such as vector search versus semantic search, embedding models, vector DB, chunking using a simple use case and finally bring all of that together into rack architecture. We'll then look into caching, monitoring and error handling techniques and close with exploring a brief setup of deploying rack in production. But that's not all. This is not just a theory course. We have hands-on labs after each lecture that will help you practice what you learned. Our labs open up instantly right in the browser. So there is no need to spend time setting up an environment. These labs are staged with challenges that will help you think and learn by doing and it comes absolutely free with this course. I'll let you know how to go about the labs when we hit our first lab session. For now, let's start with the first topic. Let's start with the simplest explanation of rag. Say you were to ask Chad GBT what's the reimbursement policy for home office setup. You already know when you ask this question that Chad GBT is going to give an incorrect answer because it doesn't have access to our policy document that's private to our company. So an LLM like GPT would hallucinate and provide an incorrect or generic answer that's common to most companies. The problem here is that it doesn't have the necessary context of what you're asking about.