Data Frameworks for Generative AI
By External Provider
About this course
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.
What you will learn
- How structured and unstructured data duel GenAI applications, and how to prepare each for LLM consumption.
- How to build retrieval-aware architectures and select context sources that improve factuality in output.
- How to craft customized taxonomies and metadata for discoverability and compliance.
- Use dialogues, labs, and assignments to test, iterate, and document your framework decisions.