Evaluating RAG Solutions
Discover Retrieval Augmented Generation (RAG) for Developers. This course will teach you how to evaluate and implement RAG solutions to enhance the accuracy, efficiency, and relevance of information retrieval in AI applications.
What you'll learn
Current customer support systems often struggle to provide quick and accurate responses to user queries, especially those requiring specific, contextually relevant information from a vast knowledge base. In this course, Evaluating RAG Solutions, you’ll learn to implement and optimize Retrieval Augmented Generation (RAG) systems.
First, you’ll explore how to identify the specific needs and requirements for an effective RAG system. Next, you’ll discover how to evaluate and select the most suitable RAG model based on performance metrics. Finally, you’ll learn how to set up, configure, test, and optimize the RAG system for deployment. When you’re finished with this course, you’ll have the skills and knowledge of RAG solutions needed to enhance the accuracy, efficiency, and relevance of information retrieval in AI applications.
Table of contents
About the author
Luca Berton, with over 18 years in AI, cloud, and automation, is a leading expert in Ansible automation, renowned for his practical experience and educational impact. His career includes a significant role as Vice President of Infrastructure at JPMorgan Chase & Co., where he spearheaded automation initiatives to shape future corporate infrastructure. Luca’s journey also features pioneering Smart City solutions at City Green Light and architecting scalable Amazon EC2 Linux instances at Studio Sto... more