Large language models

사내 LLM 게이트웨이를 설계하고 운영하는 실무서입니다. 키리스 인증과 6단계 RBAC, 토큰 쿼터·예산과 비용 통제, 엔터프라이즈 규모 MCP, 운영 런북까지 다룹니다. 영문판 The AI Gateway Playbook의 한국어판(PDF 75쪽)입니다.

A practitioner's playbook for building an in-house LLM gateway: keyless auth, six-tier RBAC, quotas and budgets, per-token cost control, MCP at enterprise scale, and the operations to run it all. About 32,000 words with diagrams, configs, and checklists.

You watched a language model do half an hour of work on its own. This book is about the question that came next: how much of it can you trust?

Your own AI assistant at home: private, local, and safe. Ten step-by-step chapters, each one tested on a fresh machine before publishing.

Build one real AI system end to end, from raw job postings to a deployed, tested API, and learn production AI engineering on the way. 24 chapters, runnable code, and numbers you can reproduce.

Build a real LLM inference engine from the ground up in portable C. Learn how transformers work, load SafeTensors weights, implement KV caching and optimize inference on the CPU. By the end, you will run TinyLlama locally and generate text with a complete working runtime.

Get more from Claude Sonnet 5.5 with practical prompting techniques that actually work. This guide explores instruction design, effort control, tool use, structured outputs and agentic workflows, with clear distinctions between documented behavior and practical experience. Built for developers, prompt engineers and advanced users.

What if you could build an AI that writes just like you? In Building Your LLM Twin, you'll follow a hands-on journey from raw writing data to a fully deployed AI that captures your unique voice. Learn to build, fine-tune and scale real-world LLM systems with practical code, proven techniques and the tools to take your project from concept to production.

Unlock the full potential of large language models with RAG. This book takes you from core concepts to production-ready systems, exploring everything from embeddings and search to evaluation and security. With clear explanations, runnable code and practical insights, you'll learn how to build reliable AI systems grounded in the knowledge that matters.

A practical guide to building reliable LLM integrations that hold up in production. Learn how to design provider-agnostic APIs, handle failures, add observability and security, and keep your architecture maintainable. With a complete Python codebase, this book turns proven patterns into working software.

Discover the algorithms behind intelligent machines.Artificial Intelligence: Algorithms and Applications Unveiled takes readers from the fundamental concepts of AI to the algorithms and applications shaping modern technology. Explore machine learning, neural networks, deep learning, natural language processing, computer vision, robotics, healthcare, finance, business intelligence, and more.Designed to connect theory with practical understanding, the book explains how AI algorithms work, where they can be applied, and what challenges must be considered when building intelligent systems.From learning algorithms to responsible AI, this book provides a structured pathway for students, developers, researchers, educators, and technology professionals who want to understand the rapidly evolving world of Artificial Intelligence.Learn the algorithms. Understand the applications. Think intelligently about AI.

What happens when AI moves beyond answering questions and begins to pursue goals?Agentic AI: The Rise of Autonomous, Goal-Driven Intelligence explores the emerging world of intelligent systems that can perceive, reason, plan, learn, collaborate, and act within defined environments.From the foundations of autonomous agents and multi-agent systems to robotics, finance, logistics, security, ethics, and future research, this book provides a structured journey through the rapidly evolving landscape of Agentic AI.Discover how goal-driven intelligence works, how autonomous agents make decisions, how multiple agents collaborate, and why safety, trust, accountability, and human oversight are essential to the future of autonomous systems.Understand the agents. Explore the architectures. Examine the applications. Think about the future.

Master AI/ML System Design with a Structured Interview FrameworkLearn how to approach complex ML system design interviews using a 20-step framework, architecture blueprints, real-world case studies, scalability strategies, data and model pipelines, deployment patterns, monitoring, and interview-ready templates.

Build Smarter Chatbots and Voice AssistantsExplore Conversational AI from fundamentals to real-world applications. Learn NLP, intent and context management, intelligent chatbots, voice assistants, speech technologies, multimodal interfaces, APIs, deployment, business integration, analytics, ethics, LLMs, and Generative AI.

Secure AI. Understand the Threats. Build More Trustworthy Intelligent Systems.Explore adversarial attacks, data poisoning, model theft, AI-enabled cyber threats, secure AI development, adversarial defenses, trustworthy AI, MLOps security, governance, privacy, red teaming, and the future of AI cybersecurity through practical concepts and real-world case studies.