Core LLM Architecture: Understanding ChatGPT, RAG, and AI Agents

Large Language Models are transforming technology, but understanding how they actually process information, retrieve data, and execute tasks can feel overwhelming. This course breaks down the complex mechanics behind ChatGPT, vector retrieval, and autonomous systems into clear, structured concepts. By reading detailed explanations and analyzing conceptual walkthroughs, you will transition from a curious user to an informed practitioner who understands the structural architecture of modern AI. What you'll learn: - Understand the fundamental transformer architecture and how models like ChatGPT predict the next token. - Master the core mechanics of Retrieval-Augmented Generation (RAG) using vector databases to ground model responses. - Analyze the structural design of AI Agents, including planning, memory, and tool execution loops. - Apply foundational prompt engineering patterns to guide model outputs systematically. - Explore modern design patterns for connecting language models to external data sources and APIs. The journey begins with essential AI terminology and foundational transformer concepts before progressing to retrieval systems and autonomous agent workflows. Through clear written explanations and structural breakdowns, you will build a complete mental model of modern language technologies. This course is designed for absolute beginners, software developers curious about AI, and tech enthusiasts looking for a conceptual foundation without needing a background in advanced mathematics. Start reading today to unlock the inner workings of modern artificial intelligence.