Overview

Foundations of Retrieval-Augmented Generation: Building Practical Pipelines, Ethical AI Workflows, and Scalable RAG Systems for Developers Large language models are powerful, but they're not enough on their own. Without real-time knowledge, they hallucinate, miss context, and struggle in production environments. Retrieval-Augmented Generation (RAG) is the solution-combining the reasoning abilities of LLMs with the precision of search and retrieval to deliver factual, reliable, and context-aware results. This book gives developers, engineers, and AI practitioners a complete roadmap for building practical and scalable RAG systems. You'll learn not just how RAG works, but how to implement it with confidence-turning theory into production-ready pipelines. From embeddings and vector databases to reranking, orchestration, and deployment, each chapter blends clear explanations with code templates you can immediately apply. Inside, you'll explore how to: Build and query vector databases with FAISS, Milvus, and Weaviate. Improve retrieval accuracy with indexing strategies, hybrid search, and cross-encoder reranking. Orchestrate pipelines using LangChain, LlamaIndex, and Haystack. Optimize for scale with caching, serverless deployment, monitoring, and observability. Address bias, hallucination, privacy, and transparency with ethical AI workflows. Apply RAG to real-world use cases including research, automation, analytics, and domain-specific assistants. Packed with reusable blueprints, evaluation strategies, and insights from real-world projects, this book moves beyond prototypes to show you how to build grounded, ethical, and production-ready AI systems. If you've ever wondered how to make LLMs accurate, trustworthy, and adaptable to your data, this book is your essential guide to Retrieval-Augmented Generation.

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