Foundations of Generative AI and Practical Prompt Engineering

As generative AI becomes a staple of modern workflows, knowing how to communicate effectively with large language models is a critical skill. Many struggle with vague outputs and unpredictable AI behavior simply because they lack a systematic approach to crafting prompts. This text-based course guides you from foundational AI concepts to practical prompting techniques, helping you transition from basic chat interactions to structured prompt design. By reading through clear explanations and analyzing practical examples, you will learn how to structure your inputs to get reliable, high-quality responses for writing, data analysis, coding, and everyday problem-solving. You will gain a clear mental model of how these systems process information, allowing you to predict and control their outputs with confidence. What you'll learn: - Understand the core mechanics of generative AI, large language models, and how they predict text. - Apply essential prompt engineering patterns, including zero-shot, few-shot, and chain-of-thought prompting. - Configure system instructions and parameters to guide model behavior, tone, and formatting. - Identify and mitigate common issues like AI hallucinations and biased outputs through structured constraints. - Explore how prompt engineering integrates with modern AI architectures like retrieval-augmented generation. - Practice writing robust prompts for real-world tasks such as summarization, code generation, and content creation. You will begin by learning key terminology and foundational definitions before moving into structured text-based exercises that challenge you to refine raw prompts into precise instructions. The material flows logically from basic concepts to advanced prompt design principles without requiring any coding background. This course is designed for absolute beginners who want to use AI tools more effectively in their professional or personal lives, with no prior technical experience required. Start reading today to unlock the full potential of modern language models.