Overview

What if you could move beyond hype and actually build production-grade generative AI systems that solve real business problems? This book is a practical, end-to-end guide to designing, building, and deploying modern Generative AI applications - from Retrieval-Augmented Generation (RAG) and vector databases to multimodal search, agentic workflows, and production APIs. It strips away the noise and focuses on what truly matters: architecture, grounding, integration, evaluation, and deployment. You won't just learn how models work - you'll learn how to engineer intelligent systems around them. Inside, you'll build document assistants, grounded chatbots, multimodal search engines, code generation tools, and AI agents that call APIs and reason step-by-step. Every concept is reinforced with complete, working code examples and real implementation patterns used in modern AI systems. By the end of this book, you will be able to: Design scalable RAG pipelines that reduce hallucinations and increase accuracy Implement vector search with embeddings and similarity indexing Build multimodal applications that combine text, images, and structured outputs Create agent-style systems that reason, plan, and use tools Deploy production-ready AI services with performance and cost awareness Evaluate, secure, and optimize your GenAI applications The unique strength of this book is its engineering focus. This is not a collection of prompt tricks. It is a system-builder's manual. You will think architecturally, write clean integration code, and understand how to move from prototype to production with confidence. Whether you are a software engineer, technical founder, AI enthusiast, or backend developer looking to stay ahead of the curve, this book equips you with the practical skills required in today's rapidly evolving AI landscape. Generative AI is no longer optional. It is infrastructure. If you are ready to stop experimenting and start building serious AI systems - turn the page and begin.

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