RAG Mastery - Build Intelligent Search Systems : A Hands-On Guide to Retrieval-Augmented Generation with Python
Language: English
Published by Amazon Digital Services LLC - Kdp Jul 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 48.80
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Add to basketItem description from seller
Neuware - Build AI systems that answer with facts-not hallucinations.Retrieval-Augmented Generation (RAG) has become the foundation of modern AI applications, powering intelligent assistants, enterprise search engines, document question-answering systems, customer support bots, legal research platforms, and knowledge management solutions.RAG Mastery is a practical, code-first guide that teaches you how to design, build, optimize, and deploy production-ready Retrieval-Augmented Generation systems using Python and today's most powerful AI tools.Instead of overwhelming you with theory, this book walks you through complete implementations with clear explanations, real-world projects, and fully working code.Inside this book you'll learn how to: + Understand the complete RAG architecture from indexing to answer generation+ Build semantic search systems using embeddings and vector databases+ Master LangChain for modern AI application development+ Work with Chroma, FAISS, Pinecone, and Weaviate+ Load PDFs, Word documents, web pages, databases, and enterprise data+ Design effective chunking strategies for maximum retrieval quality+ Implement hybrid search, reranking, metadata filtering, and advanced retrieval techniques+ Build conversational RAG with memory and context awareness+ Evaluate RAG systems using industry-standard metrics+ Optimize latency, accuracy, scalability, and production performance+ Deploy enterprise-grade RAG applications with confidenceHands-on Projects Included- Intelligent Document Question-Answering System- Enterprise Knowledge Base Assistant- Multi-Source AI Research AssistantEvery chapter includes practical examples, production-ready Python code, best practices, troubleshooting advice, architectural insights, and implementation techniques that can be immediately applied to real-world AI products.Whether you're an AI engineer, Python developer, software architect, machine learning practitioner, data scientist, or computer science student, this book provides the skills needed to build reliable AI systems powered by Retrieval-Augmented Generation.If you want to move beyond chatbots and create intelligent AI applications that retrieve accurate information, reduce hallucinations, and scale to enterprise workloads, RAG Mastery is your complete guide.…
Seller Inventory # 9798188979324
- Title
- RAG Mastery - Build Intelligent Search Systems : A Hands-On Guide to Retrieval-Augmented Generation with Python
- Author
- Teli Publications
- Publisher
- Amazon Digital Services LLC - Kdp Jul 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798188979324
- Item weight
- 381 grams
- Dimensions
- 229x152x15 mm
- Series
- Book 17 of 19: The AI Masterclass Series
Build AI systems that answer with facts—not hallucinations.
Retrieval-Augmented Generation (RAG) has become the foundation of modern AI applications, powering intelligent assistants, enterprise search engines, document question-answering systems, customer support bots, legal research platforms, and knowledge management solutions.
RAG Mastery is a practical, code-first guide that teaches you how to design, build, optimize, and deploy production-ready Retrieval-Augmented Generation systems using Python and today's most powerful AI tools.
Instead of overwhelming you with theory, this book walks you through complete implementations with clear explanations, real-world projects, and fully working code.
Inside this book you'll learn how to:
† Understand the complete RAG architecture from indexing to answer generation
† Build semantic search systems using embeddings and vector databases
† Master LangChain for modern AI application development
† Work with Chroma, FAISS, Pinecone, and Weaviate
† Load PDFs, Word documents, web pages, databases, and enterprise data
† Design effective chunking strategies for maximum retrieval quality
† Implement hybrid search, reranking, metadata filtering, and advanced retrieval techniques
† Build conversational RAG with memory and context awareness
† Evaluate RAG systems using industry-standard metrics
† Optimize latency, accuracy, scalability, and production performance
† Deploy enterprise-grade RAG applications with confidence
Hands-on Projects Included• Intelligent Document Question-Answering System
• Enterprise Knowledge Base Assistant
• Multi-Source AI Research Assistant
Every chapter includes practical examples, production-ready Python code, best practices, troubleshooting advice, architectural insights, and implementation techniques that can be immediately applied to real-world AI products.
Whether you're an AI engineer, Python developer, software architect, machine learning practitioner, data scientist, or computer science student, this book provides the skills needed to build reliable AI systems powered by Retrieval-Augmented Generation.
If you want to move beyond chatbots and create intelligent AI applications that retrieve accurate information, reduce hallucinations, and scale to enterprise workloads, RAG Mastery is your complete guide.
"Synopsis" may belong to another edition of this title.
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