Overview

In this authoritative, hands-on guide, you'll learn how to design, build, and maintain real-world AI applications using local large language models (LLMs) like Ollama. This book isn't about theory or buzzwords. It's a deep, technical roadmap written for developers who want to take control of their AI stack, eliminate dependency on remote APIs, and engineer systems that are private, reliable, maintainable, and production-ready. Whether you're building a local assistant, a document understanding engine, a code productivity tool, or an offline embedded AI device, this book teaches you how to do it right. From prompt design and inference workflows to security, governance, scaling, and long-lived operations, you'll find practical insights and ready-to-run examples that bridge the gap between toy experiments and real products. What You'll Learn You'll dive into topics including: - Why local inference matters and when to choose it over cloud services - How to structure prompts, debug unexpected behavior, and control model outputs - Practical workflows for Python, JavaScript, and web clients - Architectures for production AI: single-node, multi-component pipelines, and agentic systems - Building document intelligence, chat systems, and code assistants with local LLMs - Security, privacy, auditing, and governance patterns for enterprise use - Deployment strategies for desktop software, edge devices, and server-based environments - Strategies for monitoring, logging, and maintaining long-lived AI systems Every chapter walks you through real, runnable examples, clear explanations, and actionable patterns that work in the real world. You'll discover not just what to build, but how and why it works, and how to engineer systems that scale without surprising you. Who This Book Is For This book is for professional developers, DevOps engineers, AI engineers, and technical leads who are: - Moving from single script prototypes to full-scale applications - Designing AI systems that must operate offline, privately, or in regulated environments - Responsible for performance, security, and long-term maintenance - Ready to take local AI out of demos and into production If you want to build AI systems that are predictable, efficient, secure, and truly your own, this book gives you the blueprint. Build with confidence. Deploy with control. Engineer with purpose.

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