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.

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Data Frameworks for Generative AI
This course is part of Modern Data Strategy for Enterprise Generative AI Specialization


Instructors: Fractal Analytics Academy
Included with
Recommended experience
What you'll 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.
Skills you'll gain
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There are 2 modules in this course
Explore the foundational role of data frameworks in GenAI; how LLMs, RAG, and agentic AI rely on governed data; and the pillars of GenAI data strategy.
What's included
6 videos5 readings3 assignments
Design robust, taxonomy led frameworks; apply Responsible AI governance; and future proof enterprise data for upcoming GenAI deployments.
What's included
7 videos4 readings3 assignments2 ungraded labs
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Frequently asked questions
It provides a focused introduction to Responsible AI and its application to Generative AI, helping you build trust and compliance in modern AI systems.
Professionals responsible for data architecture, governance, and AI productization (data leaders, architects, ML engineers, PMs, consultants)
Analyze GenAI data needs; design comprehensive frameworks; create/customize taxonomies; embed Responsible AI governance; and operationalize GenAI‑ready foundations.
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Financial aid available,

