Mastering Retrieval-Augmented Generation (RAG): Build and Deploy RAG Systems with Vector Databases, Hybrid Search, and LLM Pipelines - Couverture souple

Livre 3 sur 3: Mastering LLMs Series

Langford, Derek

 
9798296901736: Mastering Retrieval-Augmented Generation (RAG): Build and Deploy RAG Systems with Vector Databases, Hybrid Search, and LLM Pipelines
Couverture souple
ISBN 13 : 9798296901736
Editeur : Independently published, 2025

Synopsis

Unlock the full power of large language models with Mastering Retrieval-Augmented Generation (RAG). This hands-on guide walks you through building production-grade RAG systems that fuse transformer-based LLMs with cutting-edge vector databases, keyword-and-semantic hybrid search, and custom pipelines. You’ll start by mastering the core concepts of retrieval vs. generation, then dive into real-world tools—LlamaIndex, LangChain, Pinecone, Weaviate, and ChromaDB—to index, retrieve, and refine context at scale.

Each chapter is packed with proven recipes and end-to-end projects: from a legal-document QA assistant to a personalized news summarizer and a compliant healthcare chatbot. Learn how to optimize context injection, implement PEFT/LoRA fine-tuning, safeguard data privacy (GDPR/HIPAA), and deploy auto-scaling microservices with CI/CD. Detailed performance-tuning, monitoring strategies, and cost-management best practices ensure you deliver low-latency, high-accuracy applications.

Whether you’re an ML engineer, data scientist, or technical lead, this book arms you with the architecture patterns, code examples, and operational know-how to launch RAG-powered AI applications that users love—and trust. Dominate the RAG niche on Amazon and in production.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Éditeur
Independently published
Date d'édition
2025
Langue
anglais
ISBN 13 
9798296901736
Reliure
Broché
Nombre de pages
148
Coordonnées du fabricant
non disponible
Personne responsable
non disponible