Welcome to Module 2! We're now focusing on the first major capability of the Modular Context Protocol: Retrieve, powered by Retrieval-Augmented Generation (RAG). We will learn how RAG overcomes core LLM limitations by retrieving only the most relevant, verifiable information at query time.

This module focuses on the Context pillar of the Modular Context Protocol (MCP). We are moving beyond retrieval to give the LLM the ability to remember (Memory) and act (Agents), making it a truly stateful and useful assistant.

This module transitions the MCP architecture from a functional prototype to an enterprise-grade production system. It focuses on the "Three Horsemen of Production" to ensure AI agents are fast, trustworthy, and measurable. We will learn to implement scientific evaluation frameworks and robust defense mechanisms against malicious inputs.

In this final module, we will synthesize the Modular Context Protocol (MCP) framework, integrating RAG, Sliding Window Memory, and Agent Routing, into a single, high-performance system. This is the transition from building individual components to mastering a cost-controlled, enterprise-grade AI architecture.