Generative AI Prompting, System Evaluation, and Governance

As generative AI becomes central to modern workflows, ensuring that model outputs are reliable, safe, and aligned with organizational standards is more critical than ever. This course guides you through the foundational principles of prompt engineering, performance evaluation, and risk management. Through clear written explanations, structured examples, and practical text-based exercises, you will transition from basic prompting to deploying governed, high-performing AI solutions. You will learn how to systematically measure output quality, identify bias, and set up robust guardrails that prevent performance drift. What you'll learn: 1. Master structured prompting techniques to guide model behavior and minimize hallucinations. 2. Understand modern evaluation methodologies to measure the accuracy, bias, and toxicity of AI outputs. 3. Apply governance frameworks to manage risk, ensure data privacy, and maintain regulatory compliance. 4. Explore retrieval-augmented generation (RAG) evaluation patterns to assess system-wide reliability. 5. Design operational guardrails and monitoring strategies to detect performance degradation over time. The course starts with key terminology and foundational concepts of large language models before moving into practical prompting strategies and systematic evaluation techniques. You will conclude by exploring real-world governance frameworks and risk mitigation policies. This course is designed for beginners, business professionals, and aspiring AI practitioners who want to understand how to manage generative AI responsibly. No prior programming or machine learning experience is required. Start reading today to build a secure, reliable, and well-governed foundation for your generative AI initiatives.