Is your data strategy ready for the future of AI?

LLMs, RAG, and agentic AI demand more than just data—they need structure, governance, and clarity. Without a robust framework, even advanced models risk generating flawed outputs. This course explores how foundational data principles enable scalable, trustworthy generative AI solutions. Come along this journey and find out how structured and unstructured data power today's advanced AI applications, whether they are large language models or agentic systems. You'll explore frameworks that overcome common LLM limitations like hallucinations and outdated information through effective data governance and management. We start by analyzing LLMs’ role in today’s AI applications, addressing limitations like hallucinations and outdated context.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. Through case studies and expert-led discussions, you'll develop practical skills in designing data taxonomies and implementing enterprise-ready data foundations. By addressing the critical intersection of data quality and AI performance, this course equips you with the knowledge to build AI systems that deliver consistent, trustworthy results—skills increasingly vital as organizations scale their generative AI initiatives across business functions.