Explainable AI (XAI) For Generative AI

Disclaimer- This course contains the use of artificial intelligence

Unlock the black box of Generative AI with "Explainable AI (XAI) for Generative AI", a comprehensive course designed to bridge the gap between cutting-edge generative models and responsible, interpretable AI systems. Whether you're a data scientist, ML engineer, or AI enthusiast, this course will empower you to build and deploy transparent, accountable, and trustworthy GenAI solutions.

You’ll begin by exploring the landscape of Generative AI frameworks, understanding how they differ from traditional Large Language Models (LLMs), and when to use each. You'll get hands-on experience with Hugging Face, the leading open-source platform for accessing and working with pre-trained models across a wide variety of generative tasks.

The course introduces basic prompt engineering techniques to guide generative models effectively and predictably. From there, you'll dive into the fundamentals of Explainable AI (XAI)—what it is, why it matters, and the unique challenges it presents in generative contexts like text and image generation.

You'll learn practical methods for implementing XAI in both text-based and conditional generative systems, including techniques like attention visualization, latent space analysis, and post-hoc explainability tools such as LIME and SHAP. Finally, you’ll discover how to operationalize XAI through prompt engineering, crafting prompts that not only guide model output but also elicit transparent reasoning via Chain of Thought and other explainability-oriented prompting strategies.

By the end of the course, you'll have the skills to build more interpretable, responsible, and human-aligned generative AI systems—ready for use in production environments and high-stakes applications.

Why Should You Take My Course?

I have an MPhil (Geography and Environment) from the University of Oxford, UK. I also completed a data science PhD (Tropical Ecology and Conservation) at Cambridge University.

I have several years of experience analyzing real-life data from different sources and producing publications for international peer-reviewed journals.