Overview

The Generative AI Engineering Manual: A Practical Guide to Architecting RAG Pipelines, AI Agents, and Production LLM Systems Are you building generative AI systems, or just experimenting with prompts? It's easy to generate text with a large language model. It's much harder to architect a Retrieval-Augmented Generation (RAG) pipeline, engineer autonomous AI agents, control cost, ensure reliability, and deploy production-ready LLM systems that scale under real traffic. The Generative AI Engineering Manual is your blueprint for moving from prototype to infrastructure. This book delivers a practical, engineering-first framework for designing, building, testing, deploying, and scaling modern generative AI systems. It focuses on real-world architecture, RAG pipelines, vector databases, model selection, agent orchestration, LLMOps, monitoring, security, and cost engineering, so you can build systems that are measurable, resilient, and enterprise-ready. Instead of theory-heavy discussions or surface-level tutorials, this guide walks you through the full lifecycle of production AI systems. You'll learn how to structure ingestion pipelines, optimize embeddings and retrieval, design deterministic agent workflows, enforce API contracts, implement regression testing, and deploy with containerized and distributed architectures. By the end of this book, you will be able to: Architect scalable RAG pipelines with clean separation of ingestion, retrieval, and generation layers Engineer AI agents with structured tool integration and controlled execution depth Select models based on performance, latency, and cost trade-offs Implement automated test suites for generative outputs Deploy using Docker, Kubernetes, and CI/CD pipelines Monitor latency, token usage, hallucinations, and drift in production Apply cost optimization tactics and horizontal scaling strategies Avoid common architectural pitfalls in enterprise AI systems Whether you're a data professional, backend engineer, AI architect, or technical leader, this book equips you with the system-level thinking required to build production-grade LLM applications. Generative AI is no longer a novelty, it's infrastructure. The difference between experimentation and engineering is discipline. If you're ready to design scalable RAG systems, deploy autonomous AI agents, and build reliable production LLM platforms, this manual belongs on your desk. Start building AI systems that work, at scale.

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