Retrieval-Augmented Generation for AI
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Home > Computing and Information Technology > Computer science > Human–computer interaction > Information architecture > Retrieval-Augmented Generation for AI: Build Reliable, Up-to-Date LLMs with Real-World Knowledge
Retrieval-Augmented Generation for AI: Build Reliable, Up-to-Date LLMs with Real-World Knowledge

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


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

About the Book

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.

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Product Details
  • ISBN-13: 9798265291004
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 254 mm
  • No of Pages: 228
  • Returnable: N
  • Sub Title: Build Reliable, Up-to-Date LLMs with Real-World Knowledge
  • Width: 178 mm
  • ISBN-10: 8265291000
  • Publisher Date: 14 Sep 2025
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Spine Width: 12 mm
  • Weight: 453 gr

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