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.

Data Frameworks for Generative AI
包含在 中
您将学到什么
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.
要了解的详细信息

添加到您的领英档案
6 项作业
了解顶级公司的员工如何掌握热门技能

积累特定领域的专业知识
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 获得可共享的职业证书

该课程共有2个模块
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.
涵盖的内容
6个视频5篇阅读材料3个作业
Design robust, taxonomy led frameworks; apply Responsible AI governance; and future proof enterprise data for upcoming GenAI deployments.
涵盖的内容
7个视频4篇阅读材料3个作业2个非评分实验室
获得职业证书
将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。
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常见问题
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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提供助学金,






