Kód: 51051102
Retrieval Augmented Generation
Retrieval-Augmented Generation: Build Reliable Retrieval-Augmented Generation Systems for LLMs and Generative AIUnlock the full potential of Retrieval-Augmented Generation (RAG) with this authoritative, hands-on guide for engineer ... celý popis
22.67 €
Bežne: 24.37 €
Ušetríte 1.70 €
Skladom u dodávateľa
Odosielame za 9 - 15 dní
Pridať medzi želanie
Mohlo by sa vám tiež páčiť
Darujte túto knihu ešte dnes
- Objednajte knihu a vyberte Zaslať ako darček.
- Obratom obdržíte darovací poukaz na knihu, ktorý môžete ihneď odovzdať obdarovanému.
- Knihu zašleme na adresu obdarovaného, o nič sa nestaráte.
Viac informácií
Viac informácií o knihe Retrieval Augmented Generation
Nákupom získate 55 bodov
Anotácia knihy
Retrieval-Augmented Generation: Build 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. Retrieval-Augmented Generation 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.
Parametre knihy
- Celý názov: Retrieval Augmented Generation
- Podnázov: Build Reliable Retrieval-Augmented Generation Systems for LLMs and Generative AI
- Autor: Husn Ara
- Jazyk:
Angličtina
- Väzba: Brožovaná
- Počet strán: 340
- EAN: 9798246815908
- ID: 51051102
- Nakladateľ: Independently published
- Hmotnosť: 457 g
- Rozmery: 229 × 152 × 18 mm
- Dátum vydania: 03. February 2026