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Retrieval-Augmented Generation Mastery: Build Production-Ready Genai Applications with Langchain, Llamaindex, and Vector Databases - The Complete Engi
Paperback
Publisher: Independently Published
Published: Nov 7 2025
Pages: 224
Weight: 0.58
Height: 0.47 Width: 5.50 Depth: 8.50
Language: English
Unleash the Power of Retrieval-Augmented Generation and Build Smarter, Scalable, and Production-Ready AI Systems.
The age of static language models is over. In 2026, the future belongs to Retrieval-Augmented Generation (RAG) - a revolutionary paradigm that blends large language models (LLMs) with real-time, knowledge-retrieval pipelines to create intelligent, context-aware AI applications that truly understand your data.
In Retrieval-Augmented Generation Mastery (2026 Edition), acclaimed AI engineer and author Finn Cordex delivers the definitive guide for developers, ML engineers, and AI practitioners who want to master the art and engineering of RAG systems - from theory to real-world deployment.
Whether you're building internal knowledge assistants, enterprise search bots, or data-driven GenAI apps, this book equips you with everything you need to go from concept to production-ready intelligence.
What You'll Learn
Core RAG Foundations: Understand how retrieval and generation combine to overcome LLM hallucination and enhance factual accuracy.
Vector Database Integration: Learn how to use FAISS, Chroma, and Pinecone to build high-performance embedding pipelines.
LangChain & LlamaIndex in Action: Implement practical, end-to-end RAG systems using today's most powerful frameworks.
Advanced Architectures: Explore hybrid retrieval, multi-modal RAG, and agentic extensions for next-generation AI systems.
Optimization & Deployment: Learn evaluation, latency reduction, cost management, and production-grade scaling techniques.
Real-World Projects: Walk through hands-on implementations of RAG-based chatbots, document retrievers, and contextual search assistants.
Inside the Book
Structured for professional clarity and depth, this edition includes:
A comprehensive 2000-word introduction explaining RAG's evolution and significance in modern AI.
Nine in-depth chapters that blend conceptual rigor with working Python implementations.
Rich commentary, design insights, and code breakdowns that reflect true industry expertise.
Coverage of LangChain, LlamaIndex, FAISS, Pinecone, OpenAI APIs, and Hugging Face models - all contextualized for 2026's GenAI ecosystem.
Who This Book Is For
This is not another surface-level AI tutorial. It's a masterclass for serious builders - developers, engineers, data scientists, and researchers who want to go beyond chatbots and understand how modern AI systems retrieve, reason, and respond with precision.
If you're ready to build intelligent systems that learn from your data, not just memorize it, this book will show you how - step by step, code by code.
Why This Edition Matters
Written for the rapidly evolving 2026 landscape, this updated edition reflects the latest breakthroughs in:
LLM orchestration and agentic design patterns
Vector storage performance tuning
Memory-aware RAG pipelines
Evaluation metrics and hallucination control
No shortcuts. No outdated theory. Just practical, battle-tested knowledge for modern AI engineers.
Master the framework that defines the next generation of AI.
Master Retrieval-Augmented Generation.
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