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{ "item_title" : "Retrieval-Augmented Generation (RAG) and Vector Databases", "item_author" : [" Gus Newton "], "item_description" : "Retrieval-Augmented Generation (RAG) and Vector Databases: A Practical Guide for AI DevelopersAI is only as powerful as the information it can access. Retrieval-Augmented Generation (RAG) bridges the gap between static language models and real-time knowledge retrieval, enabling AI to generate more accurate, context-aware responses. This book provides a hands-on, practical guide to building RAG-powered AI systems using vector databases for efficient and intelligent information retrieval.From understanding how RAG improves AI responses to implementing scalable retrieval systems, this book walks you through step-by-step tutorials, real-world applications, and best practices for optimization. Whether you're developing AI-powered search engines, chatbots, or enterprise knowledge systems, this guide equips you with the skills to build robust retrieval-enhanced AI models.This book takes a deep dive into RAG and vector search, covering: How RAG enhances AI models by retrieving relevant context before generating responses.Vector databases (FAISS, Pinecone, Weaviate, and more) and their role in AI-powered search.Step-by-step implementation of RAG pipelines using Python and modern frameworks like LangChain and Haystack.Optimizing retrieval performance to improve accuracy, reduce latency, and scale AI systems.Real-world use cases in enterprise search, personalized recommendations, chatbots, healthcare, finance, and cybersecurity.By the end of this book, you'll have the practical knowledge to build, deploy, and scale RAG-based applications.Key Features of This BookComprehensive coverage of RAG, vector databases, and retrieval techniques.Practical tutorials and hands-on code examples using Python.Real-world applications for enterprise AI, e-commerce, and search engines.Step-by-step guidance on optimizing retrieval and reducing AI hallucinations.Deployment strategies for scaling RAG pipelines in cloud and on-premise environments.This book is perfect for: AI developers and data scientists building retrieval-augmented AI systems.Machine learning engineers optimizing search and retrieval models.Software developers working on chatbots, virtual assistants, and AI-powered search.AI researchers and enthusiasts interested in RAG, vector search, and LLMs.Unlock the full potential of AI-powered retrieval with Retrieval-Augmented Generation and Vector DatabasesWhether you're a beginner or an experienced AI professional, this book will equip you with the skills to build advanced, intelligent systems. Get your copy today and start building RAG-powered AI applications that deliver smarter, more accurate responses. ", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/9/79/831/491/9798314910122_b.jpg", "price_data" : { "retail_price" : "16.00", "online_price" : "16.00", "our_price" : "16.00", "club_price" : "16.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Retrieval-Augmented Generation (RAG) and Vector Databases|Gus Newton

Retrieval-Augmented Generation (RAG) and Vector Databases : A Practical Guide for AI Developers

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Overview

Retrieval-Augmented Generation (RAG) and Vector Databases: A Practical Guide for AI Developers

AI is only as powerful as the information it can access. Retrieval-Augmented Generation (RAG) bridges the gap between static language models and real-time knowledge retrieval, enabling AI to generate more accurate, context-aware responses. This book provides a hands-on, practical guide to building RAG-powered AI systems using vector databases for efficient and intelligent information retrieval.

From understanding how RAG improves AI responses to implementing scalable retrieval systems, this book walks you through step-by-step tutorials, real-world applications, and best practices for optimization. Whether you're developing AI-powered search engines, chatbots, or enterprise knowledge systems, this guide equips you with the skills to build robust retrieval-enhanced AI models.

This book takes a deep dive into RAG and vector search, covering:

  • How RAG enhances AI models by retrieving relevant context before generating responses.
  • Vector databases (FAISS, Pinecone, Weaviate, and more) and their role in AI-powered search.
  • Step-by-step implementation of RAG pipelines using Python and modern frameworks like LangChain and Haystack.
  • Optimizing retrieval performance to improve accuracy, reduce latency, and scale AI systems.
  • Real-world use cases in enterprise search, personalized recommendations, chatbots, healthcare, finance, and cybersecurity.

By the end of this book, you'll have the practical knowledge to build, deploy, and scale RAG-based applications.

Key Features of This Book

Comprehensive coverage of RAG, vector databases, and retrieval techniques.
Practical tutorials and hands-on code examples using Python.
Real-world applications for enterprise AI, e-commerce, and search engines.
Step-by-step guidance on optimizing retrieval and reducing AI hallucinations.
Deployment strategies for scaling RAG pipelines in cloud and on-premise environments.

This book is perfect for:

  • AI developers and data scientists building retrieval-augmented AI systems.
  • Machine learning engineers optimizing search and retrieval models.
  • Software developers working on chatbots, virtual assistants, and AI-powered search.
  • AI researchers and enthusiasts interested in RAG, vector search, and LLMs.

Unlock the full potential of AI-powered retrieval with Retrieval-Augmented Generation and Vector Databases Whether you're a beginner or an experienced AI professional, this book will equip you with the skills to build advanced, intelligent systems. Get your copy today and start building RAG-powered AI applications that deliver smarter, more accurate responses.

This item is Non-Returnable

Details

  • ISBN-13: 9798314910122
  • ISBN-10: 9798314910122
  • Publisher: Independently Published
  • Publish Date: March 2025
  • Dimensions: 10 x 7 x 0.28 inches
  • Shipping Weight: 0.53 pounds
  • Page Count: 130

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