Mastering Retrieval-Augmented Generation (RAG) : Build and Deploy RAG Systems with Vector Databases, Hybrid Search, and LLM Pipelines
Overview
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.
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Details
- ISBN-13: 9798296901736
- ISBN-10: 9798296901736
- Publisher: Independently Published
- Publish Date: August 2025
- Dimensions: 9 x 6 x 0.32 inches
- Shipping Weight: 0.46 pounds
- Page Count: 150
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