Retrieval-Augmented Generation for AI : Build Reliable, Up-to-Date LLMs with Real-World Knowledge
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:
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End-to-end RAG system design-architecture, workflow, and best practices for reliability and scale
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Building high-performance knowledge bases from structured, semi-structured, and unstructured sources
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Embedding model selection, hybrid search, and retrieval optimization for fast, relevant answers
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Advanced prompt engineering, context management, and real-world handling of long documents
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Deploying, monitoring, and scaling with confidence, including security, privacy, and compliance essentials
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Proven techniques for bias reduction, fairness, and transparent source attribution
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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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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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