Introduction to Large Language Models

Large language models are transforming how we interact with technology, yet understanding how they actually function can feel out of reach. This course demystifies the core concepts of generative AI and neural networks without requiring a background in advanced mathematics. You will transition from a curious observer to a knowledgeable practitioner capable of explaining model architectures, crafting effective prompts, and understanding how modern AI applications are built. What you will learn: Understand the foundational architecture of large language models and how they process natural language; Explore the mechanics of training, fine-tuning, and adapting models for specific tasks; Master prompt engineering techniques to guide model outputs with precision; Learn the basics of Retrieval-Augmented Generation and how vector databases connect models to external data; Analyze ethical considerations, bias, and safety guidelines in modern AI deployment. This text-only course starts with essential terminology and the history of natural language processing before moving into practical concepts like tokenization, context windows, and modern integration patterns. Designed entirely for beginners, this course requires no prior programming or machine learning experience. Start reading today to build your foundational knowledge of generative AI.