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{ "item_title" : "RAG Deployment Operations", "item_author" : [" Leif Thornqvist "], "item_description" : "In an era where generative AI underpins critical business decisions, the ability to produce factually accurate and fully attributable outputs is essential for maintaining trust and operational integrity. RAG Deployment Operations presents a rigorous, practice-oriented framework for structuring retrieval-augmented generation flows that incorporate systematic verification steps and robust source attribution mechanisms, ensuring reliable performance in live production systems. This authoritative guide equips technical professionals with the methodologies required to engineer end-to-end RAG solutions that minimize hallucinations while maximizing transparency. Beginning with foundational principles, the content advances through sophisticated techniques for knowledge base construction, precision retrieval optimization, and context-grounded generation. Central chapters provide detailed blueprints for embedding multi-stage verification processes-including automated claim validation, consistency checking, and uncertainty quantification-alongside comprehensive provenance tracking that documents the lineage of every piece of information contributing to an output. Readers will acquire expertise in deploying these systems within modern infrastructure environments, addressing key operational challenges such as scalability, latency management, security, and continuous evaluation. The book explores advanced patterns for monitoring retrieval and generation quality, implementing feedback-driven improvements, and preparing RAG architectures for enterprise-scale demands, including distributed reasoning and emerging paradigms like graph-enhanced retrieval. Emphasis throughout is placed on creating auditable, compliant systems that present clear source citations to end users and stakeholders. Intended for AI/ML engineers, MLOps practitioners, solution architects, and technical decision-makers who are responsible for transitioning RAG prototypes into dependable production services, this book bridges the gap between theoretical capabilities and operational excellence. It delivers actionable insights, architectural patterns, and evaluation strategies that enable teams to build AI applications characterized by demonstrable factuality and complete source accountability.Establish production-grade RAG operations that deliver trustworthy results. Purchase RAG Deployment Operations now to master the structures and processes that support factual, attributable outputs in your deployed systems.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/9/79/818/139/9798181399334_b.jpg", "price_data" : { "retail_price" : "20.00", "online_price" : "20.00", "our_price" : "20.00", "club_price" : "20.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
RAG Deployment Operations|Leif Thornqvist

RAG Deployment Operations : Structure of retrieval augmented generation flows incorporating verification steps and source attribution supporting factua

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Overview

In an era where generative AI underpins critical business decisions, the ability to produce factually accurate and fully attributable outputs is essential for maintaining trust and operational integrity. RAG Deployment Operations presents a rigorous, practice-oriented framework for structuring retrieval-augmented generation flows that incorporate systematic verification steps and robust source attribution mechanisms, ensuring reliable performance in live production systems. This authoritative guide equips technical professionals with the methodologies required to engineer end-to-end RAG solutions that minimize hallucinations while maximizing transparency. Beginning with foundational principles, the content advances through sophisticated techniques for knowledge base construction, precision retrieval optimization, and context-grounded generation. Central chapters provide detailed blueprints for embedding multi-stage verification processes-including automated claim validation, consistency checking, and uncertainty quantification-alongside comprehensive provenance tracking that documents the lineage of every piece of information contributing to an output. Readers will acquire expertise in deploying these systems within modern infrastructure environments, addressing key operational challenges such as scalability, latency management, security, and continuous evaluation. The book explores advanced patterns for monitoring retrieval and generation quality, implementing feedback-driven improvements, and preparing RAG architectures for enterprise-scale demands, including distributed reasoning and emerging paradigms like graph-enhanced retrieval. Emphasis throughout is placed on creating auditable, compliant systems that present clear source citations to end users and stakeholders. Intended for AI/ML engineers, MLOps practitioners, solution architects, and technical decision-makers who are responsible for transitioning RAG prototypes into dependable production services, this book bridges the gap between theoretical capabilities and operational excellence. It delivers actionable insights, architectural patterns, and evaluation strategies that enable teams to build AI applications characterized by demonstrable factuality and complete source accountability.
Establish production-grade RAG operations that deliver trustworthy results. Purchase RAG Deployment Operations now to master the structures and processes that support factual, attributable outputs in your deployed systems.

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Details

  • ISBN-13: 9798181399334
  • ISBN-10: 9798181399334
  • Publisher: Independently Published
  • Publish Date: June 2026
  • Dimensions: 10 x 7 x 0.49 inches
  • Shipping Weight: 0.9 pounds
  • Page Count: 232

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