RAG in Practice: Engineering Reliable Retrieval-Augmented Generation Systems for LLMs and Generative AI - Softcover

Inhaltsangabe

RAG in Practice: Engineering Reliable Retrieval-Augmented Generation Systems for LLMs and Generative AI

Unlock the full potential of Retrieval-Augmented Generation (RAG) with this authoritative, hands-on guide for engineers, AI professionals, and data scientists. RAG in Practice bridges the gap between large language models (LLMs) and enterprise knowledge systems, teaching you how to design, implement, and optimize robust, production-ready RAG pipelines.
Inside this book, you’ll master:

• RAG Fundamentals: Understand why standalone LLMs are limited, how RAG enhances reasoning, and the evolution from IR + NLP to modern retrieval-augmented systems.
• RAG System Architecture: Explore minimal and high-level pipelines, online/offline components, and data/control flow engineering.
• Embeddings & Vector Databases: Learn dense vs sparse embeddings, embedding drift, ANN algorithms, hybrid search, and large-scale vector indexing.
• Retrieval Quality Engineering: Implement similarity metrics, top-K selection, reranking with cross-encoders, and handle retrieval failures.
• Document Ingestion Pipelines: Design batch, streaming, and hybrid ingestion; handle PDFs, tables, HTML; and implement chunking strategies with overlap and context awareness.
• Data Quality & Versioning: Apply cleaning, normalization, deduplication, versioning, rollbacks, and audit strategies for enterprise-grade reliability.
• Query Processing & Intelligence: Master query classification, rewriting, multi-query retrieval, and self-querying RAG systems.
• Advanced Retrieval Techniques: Build hybrid search, temporal/context-aware retrieval, and multi-hop systems for real-world applications.

This book is packed with Python code examples, architecture diagrams, and practical guidance, so you can implement systems confidently while avoiding common production pitfalls.
Case Studies Included

• Large-Scale Vector Search – industrial vector database deployment and performance optimization.
• Enterprise Document Ingestion – handling multi-format documents at scale.
• Search-Driven RAG at Scale – hybrid search and multi-hop retrieval in production.
• RAG Retrieval Failures – diagnosis and mitigation of low recall/high hallucination scenarios.
• Knowledge Base Versioning – version control and rollback in live systems.

Whether you’re building enterprise search, AI assistants, or knowledge-grounded LLM applications, RAG in Practice provides the step-by-step blueprint to engineer high-performance, reliable, and scalable knowledge-augmented AI systems.

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