RAG & MCP: Smarter AI with Memory and Context
Welcome! In this post, we explore two key concepts for building intelligent, context-aware AI systems: Retrieval-Augmented Generation (RAG) and Model Context Protocol (MCP). These frameworks tackle a critical challenge: how to make LLMs more accurate, grounded, and capable of complex interactions. RAG: Retrieval-Augmented Generation Purpose: To empower language models with access to external knowledge at inference time. How It Works: Retriever module fetches relevant documents from a vector store or indexed database based on the user query. Generator (usually an LLM) uses both the query and retrieved documents to generate an informed response. Why It Matters: Reduces hallucination. Enables real-time factual accuracy. Supports plug-and-play updates without retraining the model. Example Use Cases: Chatbots that answer based on internal company wikis. Research assistants grounded in academic papers. Domain-specific assistants (e.g., legal, medical). MCP: Model Context Protocol Purpose: To manage multi-turn memory, task orchestration, and context flow in multi-agent or tool-augmented AI systems. ...