Managing Hallucinations in Generative AI

As Generative AI becomes deeply integrated into business and creative workflows, ensuring the accuracy of AI-generated content is more critical than ever. Unintended errors, known as hallucinations, can compromise the reliability of your systems and mislead users. This text-only course guides you through understanding why hallucinations occur and how to systematically control them. By completing this course, you will transition from blindly trusting model outputs to actively designing reliable AI systems. You will gain a clear framework for evaluating model truthfulness and implementing modern engineering solutions to keep outputs grounded. What you'll learn: - Understand the foundational mechanics of large language models and why they hallucinate. - Analyze the root causes of AI inaccuracies, from training data limitations to generation parameters. - Apply advanced prompt engineering techniques to constrain model outputs and enforce factual accuracy. - Explore Retrieval-Augmented Generation (RAG) patterns to ground model responses in verified external data. - Evaluate model outputs systematically using modern validation and testing frameworks. - Design workflows that balance automated checks with human-in-the-loop oversight. The course begins with essential terminology and the core mechanics of text generation, establishing a firm conceptual foundation. You will then progress through structured written explanations and analytical scenarios that demonstrate how to implement mitigation strategies in real-world projects. This course is designed for beginners, product managers, and developers who want to understand AI reliability. No prior programming or machine learning experience is required. Start reading today to master the core principles of AI reliability and build trustworthy Generative AI solutions.