Prompt Engineering and In-Context Learning for AI Systems

As artificial intelligence becomes central to software development, knowing how to communicate effectively with large language models is a critical skill. This text-based course demystifies how to guide AI behavior predictably and safely without retraining the underlying models. You will transition from trial-and-error prompting to systematic prompt engineering and in-context learning. By understanding how models process instructions, you will design robust AI interactions, implement reasoning frameworks, and secure your systems against common vulnerabilities. What you'll learn: Understand the core mechanics of in-context learning and how models process context windows; Apply advanced prompting techniques like few-shot learning and chain-of-thought reasoning to guide complex logic; Design structured prompts that guarantee reliable outputs like JSON for software integration; Mitigate security risks such as prompt injection and jailbreaking through defensive design patterns; Integrate external context systematically to support retrieval-augmented generation patterns. The course begins with foundational concepts of language models and context processing before advancing to structured design patterns, reasoning strategies, and essential security practices. You will learn through clear written explanations, practical conceptual breakdowns, and text-based design exercises. This course is designed for developers, product managers, and tech enthusiasts who want to build reliable AI-driven features, with no prior background in machine learning required. Start reading today to unlock the full potential of language models in your next project.