Books

Build the engineering foundations required to design and develop production-grade AI systems—from Python and FastAPI to databases, security, semantic search, RAG, LLM architecture, and AI application performance.Go beyond Python programming and start thinking like an AI Engineer.The AI Engineer — Volume I takes you from professional Python engineering to the foundations of modern AI application development.You will build the engineering knowledge required to create reliable AI systems—not just experimental notebooks or simple API calls.A project-based AI engineering book covering Python production engineering, FastAPI, PostgreSQL, authentication, testing, reliability, embeddings, semantic search, RAG, LLM architecture, AI pipelines, caching, and performance optimization.AI EngineeringAI EngineerArtificial IntelligenceGenerative AILLMLarge Language ModelsPythonFastAPIRAGRetrieval Augmented GenerationSemantic SearchEmbeddingsVector SearchLLM ApplicationsAI SystemsMachine Learning EngineeringBackend EngineeringProduction AIAI ArchitectureSoftware Engineering

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?

Get more from Gemini 4 Argon with practical prompting techniques that work on real-world tasks. This guide covers coding, research, legal and financial work, long-horizon workflows, optimization and prompt evaluation, with clear examples and strategies for getting reliable results from one of the most capable AI models available.

Build an AI app people pay for, no coding required. The SHIP Method shows non technical readers how to find a money problem, prove demand, build with AI, get users and charge.

Inference engineering is now its own discipline, with unique challenges around speed, cost, reliability and scale. This practical guide takes you from core concepts to production systems, showing how modern inference architectures work, how to optimize them and how to make better engineering decisions through measurement and trade-off analysis.

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.

What happens when artificial intelligence surpasses human intelligence across nearly every field? Super Intelligence explores how such systems could emerge, what they might be capable of and how they could reshape science, society and civilization. Grounded in research and technical evidence, this book cuts through the hype to examine what super intelligence could really mean.

机器人真正学会做饭，不是因为工程师写完了每一步，而是因为它能从身体经验、失败与恢复中形成自己的手艺。

为什么参数不大的模型，把信息放对地方就能反超更大的模型？本书以"压缩"为主线，把大模型的表示、泛化、推理、RAG 与智能体统一成一道信息管理题：什么该压、什么不能丢、什么该外置、放在哪里、何时取回。18 章、190 张原创图解，从熵一路讲到世界模型。

A Comprehensive Engineering Guide to Autonomous Agent Architectures, Multi-Agent Systems, Memory, Tool Execution, Reliability, Observability, and Production DeploymentAutonomous AI agents represent a fundamental shift from conventional conversational applications toward software systems capable of executing multi-step tasks, interacting with external tools, maintaining persistent state, coordinating specialized components, and adapting their behavior to changing conditions. Building such systems requires considerably more than connecting a language model to a prompt. It demands a disciplined approach to execution control, memory architecture, workflow orchestration, security, evaluation, and production infrastructure.Mastering AI Agents: Hands-On Production Code is a practical, implementation-focused engineering reference to designing, building, and deploying autonomous AI agent systems. Across 38 chapters, it progresses from low-level LLM runtime primitives and a zero-dependency Python agent loop to cognitive architectures, persistent memory, stateful workflows, multi-agent coordination, secure tool execution, production observability, and enterprise deployment.

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.

Que una IA tenga razón no significa que tenga permiso para actuar. Una guía práctica para diseñar sistemas de IA donde capacidad, evidencia, autorización y ejecución permanecen separadas antes de producir una consecuencia real.

Build reliable, production-ready search systems with embeddings and neural reranking. Learn how to turn semantic meaning into relevant results, refine retrieved content and deliver better context for AI-generated answers. With hands-on Python examples and practical architectures, this book takes you from core concepts to real-world RAG systems.