RAG Optimization: Accurate and Efficient LLM Applications (Generative AI LLM Theory & Research)
by Michael Sharpe, David Spuler
RAG Optimization explores the theory and research behind Retrieval-Augmented Generation for large language model applications. Focusing on accuracy and efficiency, it offers readers an introduction to key considerations in developing and understanding generative AI systems built with RAG techniques.
About This Book
RAG Optimization examines Retrieval-Augmented Generation in the context of large language model applications.
The book focuses on accuracy and efficiency, addressing central goals in the development and use of generative AI systems.
It presents material grounded in LLM theory and research for readers seeking a clearer understanding of RAG optimization.
This title is suited to readers interested in artificial intelligence, language models, and the practical foundations of modern generative AI.
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