ISBN: 9798341621718 - hands-on rag for production: design, develop, and deploy production-ready rag applications (15 resultados)

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condición: 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. …

Condición: New.

Condición: new.

Paperback. Condición: 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. …

paperback. Condición: New. Special order direct from the distributor.

Condición: NEW.

Paperback. Condición: 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. …

Taschenbuch. Condición: 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.…

Condición: New.

Paperback. Condición: 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condición: 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. …

Taschenbuch. Condición: Neu. Hands-On RAG for Production | Design, Develop, and Deploy Production-Ready RAG Applications | Forrest Bao (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2026 | O'Reilly Media | EAN 9798341621718 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu. …

Paperback. Condición: 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.…

Paperback. Condición: 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 our UK warehouse or from our Australian or US warehouses, depending on stock availability.…