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

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.