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Evaluating RAG Solutions

Retrieval-Augmented Generation (RAG) enhances LLMs by accessing external knowledge, but requires proper evaluation. This course will teach you how to assess RAG systems using comprehensive evaluation methodologies and metrics.

Nicolae Caprarescu - Pluralsight course - Evaluating RAG Solutions
by Nicolae Caprarescu

What you'll learn

Implementing Retrieval-Augmented Generation (RAG) systems without proper evaluation methods can lead to unreliable outputs and missed optimization opportunities. In this course, Evaluating RAG Solutions, you will learn essential techniques to thoroughly assess and improve RAG implementations through hands-on demos using Python and RAGAS. First, you will explore fundamental evaluation metrics for both retrieval and generation components. Next, you will discover practical evaluation frameworks and tools to systematically measure RAG performance. Finally, you will learn how to interpret evaluation results and implement targeted improvements. When you are finished with this course, you will have the skills and knowledge of RAG evaluation needed to build more accurate, reliable, and efficient RAG solutions.

Table of contents

About the author

Nicolae Caprarescu - Pluralsight course - Evaluating RAG Solutions
Nicolae Caprarescu

Nicolae has been a Software Engineer since 2013, focusing on Java and web stacks. Nicolae holds a degree in Computer Science and enjoys teaching, traveling and motorsports.

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