This article provides a clear, programmer-centric explanation of core AI concepts: Large Language Models (LLMs), Agents, Model Context Protocol (MCP), Skills, and Retrieval-Augmented Generation (RAG). It uses a relatable scenario—using Claude Code to fix a bug—to illustrate how these components work together behind the scenes. For developers transitioning into AI, this serves as a valuable primer that bridges the gap between abstract theory and practical application. The content is timely given the rapid adoption of AI coding assistants and offers a structured way to understand the ecosystem. It avoids deep technical jargon, making it accessible while still providing meaningful depth for engineers.
A programmer-friendly guide to understanding LLM, Agent, MCP, Skill, and RAG, using real-world AI tool interactions as examples.