Introduction to Large Language Models and Prompt Engineering

Large Language Models are transforming how we build software, analyze data, and automate daily tasks. To leverage their full potential, you need to understand how these models process information and how to communicate with them effectively. This text-based course guides you from foundational machine learning concepts to practical application in modern software development. You will transition from a curious user to an informed builder who understands the mechanics of generative AI. By reading through clear explanations and structured examples, you will learn how to craft precise prompts, understand model limitations, and explore advanced integration patterns. What you'll learn: - Understand the core architecture of transformer models and how they predict text - Master prompt engineering techniques including few-shot prompting and chain-of-thought reasoning - Explore the differences between fine-tuning, pre-training, and Retrieval-Augmented Generation (RAG) - Manage model limitations such as hallucination, bias, and context window constraints - Analyze modern AI security concepts including prompt injection and data privacy considerations The course begins with essential terminology and the history of natural language processing before moving into hands-on prompt design workflows and architectural patterns. You will progress from basic text generation to understanding how to ground model responses with external data. This course is designed for absolute beginners, software developers, and product managers who want to understand generative AI without needing a background in advanced mathematics. No prior programming experience is required. Start reading today to build a strong foundation in modern language model technology.