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Course
- AI
Integrating Knowledge Bases for RAGs
Discover the power of integrating knowledge bases with retrieval augmented generation (RAG) models. This course will teach you how to align unstructured text data with structured knowledge bases and incorporate external data sources.
What you'll learn
Integrating knowledge bases with retrieval augmented generation (RAG) models can significantly enhance the quality and accuracy of information retrieval and generation.
In this course, Integrating Knowledge Bases for RAGs, you’ll learn to effectively align unstructured text data with structured knowledge bases, incorporate external data sources, and implement advanced knowledge integration techniques.
First, you’ll explore the techniques for aligning and linking unstructured text data with structured knowledge bases. Next, you’ll discover methods for incorporating external data sources, such as encyclopedic knowledge, domain-specific databases, and web-scale corpora, into RAG models. Finally, you’ll learn how to implement and evaluate knowledge integration approaches, including entity linking, knowledge graph embeddings, and knowledge-guided retrieval.
When you’re finished with this course, you’ll have the skills and knowledge of integrating knowledge bases with RAG models needed to enhance retrieval and generation capabilities in your projects.
Table of contents
About the author
Dr. Daniel “Brian” Letort is a 22+ year veteran of Information Technology. During a 21-year tenure at Northrop Grumman, Brian held various roles across software engineering, systems engineering, Chief Applications Architect, Chief Data Scientist, and Chief Enterprise Architect. Brian held the NG Fellow title for six years and Technical Fellow title for four years prior. In 2022, Brian joined Digital Realty as the Chief Architect - Product and Artificial Intelligence. Aside from working at Digital Realty, Brian has 12+ years experience in teaching Data Science and Computer Science classes as an adjunct professor. Brian has authored two books and holds two patents.
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