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EBOOK OReilly.Media.Inc.-.Hands-On.RAG.For.Production.2026.RETAIL.EPUB.eBook-CTO CTO 2h ago nfo url

Release Detail

OReilly.Media.Inc.-.Hands-On.RAG.For.Production.2026.RETAIL.EPUB.eBook-CTO

EBOOK CTO 2h ago

NFO

han030d6.nfo

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                              O'Reilly Media, Inc                               

                               Ofer Mendelevitch                                
                          Hands-On RAG For Production                           
                                      N/A                                       

                                      2026                                      

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      Publisher > O'Reilly Media, Inc
      Author    > Ofer Mendelevitch
      Title     > Hands-On RAG For Production
      Issue     > N/A
      Year      > 2026

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      ISBN      > 9798341621718
      Pages     > 567
      Genre     > Retrieval-Augmented-Generation-RAG
      Language  > English

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      Type      > RETAIL
      Media     > Book
      Format    > .EPUB
      Section   > eBook


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      Published > 2026-06
      Release   > 2026-05-30
      Disks     > 02*5.0MB

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https://bookshop.org/p/books/hands-on-rag-for-production-design-develop-and-
deploy-production-ready-rag-applications-forrest-sheng-bao/435f76d7993c1bd0

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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CRC32: cabf37932bb65025f6c59a196292b02731d2b54b

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