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{ "item_title" : "Retrieval-Augmented Generation for AI", "item_author" : [" William L. Younker "], "item_description" : "Retrieval-Augmented Generation for AI: Build Reliable, Up-to-Date LLMs with Real-World KnowledgeAre you tired of AI models that hallucinate, fail to cite sources, or fall behind on the latest knowledge? In a world where accuracy, transparency, and trust are non-negotiable, the standard approach to language models just isn't enough.Retrieval-Augmented Generation for AI: Build Reliable, Up-to-Date LLMs with Real-World Knowledge delivers a proven framework for engineers, architects, and leaders who need answers they can trust. This comprehensive guide is the essential resource for building AI solutions that stay current, grounded, and auditable-no matter how fast the information landscape changes.Whether you're modernizing enterprise search, deploying an intelligent chatbot, or powering a next-generation virtual assistant, this book shows you step-by-step how to connect language models with dynamic, high-quality data. Discover practical strategies for seamless retrieval, powerful prompt engineering, and context integration-backed by real code, robust patterns, and production-tested tools like LangChain, LlamaIndex, Pinecone, and Haystack.Inside, you'll master:End-to-end RAG system design-architecture, workflow, and best practices for reliability and scaleBuilding high-performance knowledge bases from structured, semi-structured, and unstructured sourcesEmbedding model selection, hybrid search, and retrieval optimization for fast, relevant answersAdvanced prompt engineering, context management, and real-world handling of long documentsDeploying, monitoring, and scaling with confidence, including security, privacy, and compliance essentialsProven techniques for bias reduction, fairness, and transparent source attributionOperational checklists, troubleshooting guides, and hands-on case studies for immediate resultsReady to deliver AI that stays accurate, up-to-date, and worthy of user trust? This is the definitive handbook for anyone serious about retrieval-augmented generation. Stop relying on guesswork and take control of your language model's output-get the clarity, performance, and transparency your users demand.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/9/79/826/529/9798265291004_b.jpg", "price_data" : { "retail_price" : "21.00", "online_price" : "21.00", "our_price" : "21.00", "club_price" : "21.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Retrieval-Augmented Generation for AI|William L. Younker

Retrieval-Augmented Generation for AI : Build Reliable, Up-to-Date LLMs with Real-World Knowledge

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Overview

Retrieval-Augmented Generation for AI: Build Reliable, Up-to-Date LLMs with Real-World Knowledge

Are you tired of AI models that hallucinate, fail to cite sources, or fall behind on the latest knowledge? In a world where accuracy, transparency, and trust are non-negotiable, the standard approach to language models just isn't enough.

Retrieval-Augmented Generation for AI: Build Reliable, Up-to-Date LLMs with Real-World Knowledge delivers a proven framework for engineers, architects, and leaders who need answers they can trust. This comprehensive guide is the essential resource for building AI solutions that stay current, grounded, and auditable-no matter how fast the information landscape changes.

Whether you're modernizing enterprise search, deploying an intelligent chatbot, or powering a next-generation virtual assistant, this book shows you step-by-step how to connect language models with dynamic, high-quality data. Discover practical strategies for seamless retrieval, powerful prompt engineering, and context integration-backed by real code, robust patterns, and production-tested tools like LangChain, LlamaIndex, Pinecone, and Haystack.

Inside, you'll master:

  • End-to-end RAG system design-architecture, workflow, and best practices for reliability and scale

  • Building high-performance knowledge bases from structured, semi-structured, and unstructured sources

  • Embedding model selection, hybrid search, and retrieval optimization for fast, relevant answers

  • Advanced prompt engineering, context management, and real-world handling of long documents

  • Deploying, monitoring, and scaling with confidence, including security, privacy, and compliance essentials

  • Proven techniques for bias reduction, fairness, and transparent source attribution

  • Operational checklists, troubleshooting guides, and hands-on case studies for immediate results

Ready to deliver AI that stays accurate, up-to-date, and worthy of user trust? This is the definitive handbook for anyone serious about retrieval-augmented generation. Stop relying on guesswork and take control of your language model's output-get the clarity, performance, and transparency your users demand.

This item is Non-Returnable

Details

  • ISBN-13: 9798265291004
  • ISBN-10: 9798265291004
  • Publisher: Independently Published
  • Publish Date: September 2025
  • Dimensions: 10 x 7 x 0.48 inches
  • Shipping Weight: 0.89 pounds
  • Page Count: 228

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