Large Language Models: Fundamentals and Conceptual Self-Assessment

Are you looking to demystify the technology behind modern artificial intelligence? Understanding how Large Language Models (LLMs) work is the first step toward leveraging their power effectively in your work or studies. This text-only course guides you from the absolute basics of natural language processing to the core mechanics of modern generative AI, helping you verify your progress at every step. You will learn the essential terminology, architectural concepts, and practical strategies needed to work with these models confidently. What you'll learn: Learn the foundational concepts of neural networks, transformers, and self-attention mechanisms; Understand how tokenization, embeddings, and context windows shape model inputs and outputs; Apply effective prompt engineering techniques, including system prompts and few-shot learning; Differentiate between pre-training, fine-tuning, and Retrieval-Augmented Generation (RAG); Evaluate LLM performance and understand common limitations like hallucinations and bias; Practice your knowledge with comprehensive written self-assessment questions and scenario analyses. The course begins with clear definitions of key AI terminology before moving into transformer architecture and training methodologies. You will then explore practical deployment concepts and conclude with a series of structured written review exercises designed to solidify your conceptual understanding. This course is designed for curious beginners, tech enthusiasts, and business professionals who want a clear, jargon-free introduction to LLMs without needing a background in advanced mathematics or coding. Start reading today to master the core principles of large language models and confidently discuss generative AI.