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Hands-On RAG for Production: Design, Develop, and Deploy Production-Ready RAG Applications - Softcover
Synopsis
Retrieval-augmented generation (RAG) is the go-to strategy for integrating large language models with your organization's unique knowledge. However, the market is full of RAG pipelines and components, making it hard to choose the right solution for your enterprise's needs. This book simplifies the process, offering a comprehensive road map to building, refining, and scaling production-grade RAG applications. Authors Ofer Mendelevitch and Forrest Bao guide you through every phase of development, from data ingestion, embeddings, and vector search to advanced techniques like agentic RAG, multimodal RAG, and GraphRAG. Engineers and architects will learn how to tackle the challenges they'll encounter when building RAG applications at enterprise scale: ensuring high accuracy with minimal hallucinations, maintaining low-latency performance, safeguarding data privacy, and providing transparent, explainable responses among them. Determine whether to build RAG yourself or deploy a RAG-as-a-service platform Build a basic RAG stack that maximizes performance and cost-effectiveness Measure key metrics such as hallucinations, response quality, latency, and cost Address challenges in enterprise deployment, such as compliance with data security and privacy requirements, explainability, and prompt design Implement advanced techniques such as multimodal RAG, agentic RAG, and GraphRAG
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About the Author
Ofer Mendelevitch leads developer relations at Vectara. He has extensive hands-on experience in machine learning, data science and big data systems across multiple industries, and has focused on developing products using large language models since 2019. Prior to Vectara, he built and led data science teams at Syntegra, Helix, Lendup, Hortonworks, and Yahoo! Ofer holds a B.Sc. in computer science from Technion and M.Sc. in EE from Tel Aviv university, and is the author of "Practical data science with Hadoop" (Addison Wesley). Forrest Sheng Bao co-leads the Machine Learning team at Vectara. He has over 10+ years of research experience in the areas of Artificial Intelligence (AI) and Natural Language Processing (NLP). Prior to Vectara, he was an assistant professor at Iowa State University. Forrest holds a PhD in computer science with a minor in electrical engineering from Texas Tech University.
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Paperback. Condition: New. Retrieval-augmented generation (RAG) is the go-to strategy for integrating large language models with your organization's unique knowledge. However, the market is full of RAG pipelines and components, making it hard to choose the right solution for your enterprise's needs. This book simplifies the process, offering a comprehensive road map to building, refining, and scaling production-grade RAG applications.Authors Ofer Mendelevitch and Forrest Bao guide you through every phase of development, from data ingestion, embeddings, and vector search to advanced techniques like agentic RAG, multimodal RAG, and GraphRAG. Engineers and architects will learn how to tackle the challenges they'll encounter when building RAG applications at enterprise scale: ensuring high accuracy with minimal hallucinations, maintaining low-latency performance, safeguarding data privacy, and providing transparent, explainable responses among them.Determine whether to build RAG yourself or deploy a RAG-as-a-service platformBuild a basic RAG stack that maximizes performance and cost-effectivenessMeasure key metrics such as hallucinations, response quality, latency, and costAddress challenges in enterprise deployment, such as compliance with data security and privacy requirements, explainability, and prompt designImplement advanced techniques such as multimodal RAG, agentic RAG, and GraphRAG. Seller Inventory # LU-9798341621718
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Paperback. Condition: new. Paperback. Retrieval-augmented generation (RAG) is the go-to strategy for integrating large language models with your organization's unique knowledge. However, the market is full of RAG pipelines and components, making it hard to choose the right solution for your enterprise's needs. This book simplifies the process, offering a comprehensive road map to building, refining, and scaling production-grade RAG applications.Authors Ofer Mendelevitch and Forrest Bao guide you through every phase of development, from data ingestion, embeddings, and vector search to advanced techniques like agentic RAG, multimodal RAG, and GraphRAG. Engineers and architects will learn how to tackle the challenges they'll encounter when building RAG applications at enterprise scale: ensuring high accuracy with minimal hallucinations, maintaining low-latency performance, safeguarding data privacy, and providing transparent, explainable responses among them.Determine whether to build RAG yourself or deploy a RAG-as-a-service platformBuild a basic RAG stack that maximizes performance and cost-effectivenessMeasure key metrics such as hallucinations, response quality, latency, and costAddress challenges in enterprise deployment, such as compliance with data security and privacy requirements, explainability, and prompt designImplement advanced techniques such as multimodal RAG, agentic RAG, and GraphRAG Authors Ofer Mendelevitch and Forrest Bao guide you through every phase of development, from data ingestion, embeddings, and vector search to advanced techniques like agentic RAG, multimodal RAG, and GraphRAG. This book simplifies the process, offering a comprehensive road map to building, refining, and scaling production-grade RAG applications. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798341621718
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Paperback. Condition: New. Retrieval-augmented generation (RAG) is the go-to strategy for integrating large language models with your organization's unique knowledge. However, the market is full of RAG pipelines and components, making it hard to choose the right solution for your enterprise's needs. This book simplifies the process, offering a comprehensive road map to building, refining, and scaling production-grade RAG applications.Authors Ofer Mendelevitch and Forrest Bao guide you through every phase of development, from data ingestion, embeddings, and vector search to advanced techniques like agentic RAG, multimodal RAG, and GraphRAG. Engineers and architects will learn how to tackle the challenges they'll encounter when building RAG applications at enterprise scale: ensuring high accuracy with minimal hallucinations, maintaining low-latency performance, safeguarding data privacy, and providing transparent, explainable responses among them.Determine whether to build RAG yourself or deploy a RAG-as-a-service platformBuild a basic RAG stack that maximizes performance and cost-effectivenessMeasure key metrics such as hallucinations, response quality, latency, and costAddress challenges in enterprise deployment, such as compliance with data security and privacy requirements, explainability, and prompt designImplement advanced techniques such as multimodal RAG, agentic RAG, and GraphRAG. Seller Inventory # LU-9798341621718
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Paperback. Condition: New. Retrieval-augmented generation (RAG) is the go-to strategy for integrating large language models with your organization's unique knowledge. However, the market is full of RAG pipelines and components, making it hard to choose the right solution for your enterprise's needs. This book simplifies the process, offering a comprehensive road map to building, refining, and scaling production-grade RAG applications.Authors Ofer Mendelevitch and Forrest Bao guide you through every phase of development, from data ingestion, embeddings, and vector search to advanced techniques like agentic RAG, multimodal RAG, and GraphRAG. Engineers and architects will learn how to tackle the challenges they'll encounter when building RAG applications at enterprise scale: ensuring high accuracy with minimal hallucinations, maintaining low-latency performance, safeguarding data privacy, and providing transparent, explainable responses among them.Determine whether to build RAG yourself or deploy a RAG-as-a-service platformBuild a basic RAG stack that maximizes performance and cost-effectivenessMeasure key metrics such as hallucinations, response quality, latency, and costAddress challenges in enterprise deployment, such as compliance with data security and privacy requirements, explainability, and prompt designImplement advanced techniques such as multimodal RAG, agentic RAG, and GraphRAG. Seller Inventory # LU-9798341621718
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Hands-On RAG for Production : Design, Develop, and Deploy Production-Ready RAG Applications
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Taschenbuch. Condition: Neu. Neuware - Retrieval-augmented generation (RAG) is the go-to strategy for integrating large language models with your organization's unique knowledge. However, the market is full of RAG pipelines and components, making it hard to choose the right solution for your enterprise's needs. This book simplifies the process, offering a comprehensive road map to building, refining, and scaling production-grade RAG applications. Authors Ofer Mendelevitch and Forrest Bao guide you through every phase of development, from data ingestion, embeddings, and vector search to advanced techniques like agentic RAG, multimodal RAG, and GraphRAG. Engineers and architects will learn how to tackle the challenges they'll encounter when building RAG applications at enterprise scale: ensuring high accuracy with minimal hallucinations, maintaining low-latency performance, safeguarding data privacy, and providing transparent, explainable responses among them. - Determine whether to build RAG yourself or deploy a RAG-as-a-service platform - Build a basic RAG stack that maximizes performance and cost-effectiveness - Measure key metrics such as hallucinations, response quality, latency, and cost - Address challenges in enterprise deployment, such as compliance with data security and privacy requirements, explainability, and prompt design - Implement advanced techniques such as multimodal RAG, agentic RAG, and GraphRAG. Seller Inventory # 9798341621718