Generative AI systems—large language models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI—demand modern data strategies to ensure accuracy and reliability. These technologies hinge on high-quality, well-governed data; without a robust framework, even advanced models risk generating flawed outputs. This course explores how foundational data principles enable scalable, trustworthy generative AI solutions.

We start by analyzing LLMs’ role in today’s AI applications, addressing limitations like hallucinations and outdated context. Next, we examine how RAG enhances LLMs with retrieval mechanisms, and why agentic AI—enabling autonomous reasoning and decision-making—is the next frontier. Each evolution underscores the criticality of structured, governed data. You’ll learn the core components of modern data strategy: unified frameworks, effective management, and principled governance. We dissect how structured and unstructured data uniquely power AI systems and introduce pillars like accessibility, security, lineage, and scalability. Through case studies, hands-on exercises, and expert-led discussions, you’ll gain practical insights into data taxonomies, classification, and real-world implementation. By the end of the course, you’ll master applying these strategies to GenAI projects, ensuring systems are built on reliable, enterprise-ready data foundations. This course is for Data Scientists, Data Engineers, AI or GenAI Leaders, and any curious learner who wants to understand modern data strategies, especially data frameworks and their impact and applications. By the end of this course, you will be able to, explain the components of a modern data framework and its role in GenAI. You will also be able to differentiate between structured and unstructured data in AI implementations and apply foundational data governance and management principles to support scalable GenAI solutions.