Data Frameworks for Generative AI

Modern GenAI (LLMs, RAG, agentic AI) succeeds or fails on the quality, structure, and governance of the data behind it. In this course, you’ll learn how structured and unstructured data drive GenAI applications, and how to design comprehensive data frameworks, taxonomies, and governance practices that reduce hallucinations, improve relevance, and make AI outcomes reliable. You’ll examine LLM limitations, connect them to data quality and metadata strategy, and implement taxonomy led architectures that future proof enterprise AI. Through case studies, practice assignments, and guided dialogues, you’ll develop the skills to design, validate, and operationalize GenAI ready data foundations for real products and platforms. By the end, you’ll be able to create enterprise grade data frameworks that deliver consistent, ethical, and high performing results

• Understand how structured and unstructured data support generative AI applications
• Build retrieval-aware architectures to enhance output factuality
• Create customized taxonomies and metadata for compliance and discoverability
• Test, iterate, and document your data framework decisions using practical exercises

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work