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This book teaches you to design reliable LLM-powered systems by focusing on the concepts, architectures, and design patterns that will stay stable even as models and APIs change. You'll learn to structure prompts, compose modular chains, and build RAG pipelines that ingest documents, split them into chunks, embed them, retrieve the right context, and ground answers to eliminate (or vastly reduce) hallucinations.
Along the way you'll build concrete applications—summarization and Q&A engines, context-aware
chatbots with memory, and tool-using AI agents that orchestrate multi-step workflows with branching logic. For the examples, the book uses Python, LangChain, LangGraph, and LangSmith, but you'll be able to generalize to other frameworks. You'll understand with clarity and confidence how to keep integrations maintainable, manage context limits and cost/latency tradeoffs, and evaluate, debug, and monitor behavior so your systems work in production. [Read more]
AI agent technology is changing fast! This totally revised Second Edition of AI Agents in Action by Micheal Lanham guides you through the latest breakthroughs in large language model (LLM)-powered autonomy, from the Model Context Protocol (MCP) to advanced multi-agent collaboration and deployment.
AI Agents in Action, Second Edition provides a solid foundation for readers just starting with agents, and it also introduces more advanced techniques and tools for experienced AI developers. You'll work through the core
layers of an agentic system, including prompt engineering, working with tools, reasoning and planning, knowledge and memory, evaluation, and feedback patterns. Each chapter combines practical, real-world examples that cover reasoning frameworks like ReAct and Sequential Thinking MCP, advanced RAG systems, and multi-agent collaboration patterns. Plus, you'll find extensive tips, tricks, and best practice advice taken from Micheal Lanham's extensive AI career. [Read more]
5 chapters of this MEAP are available now with more to follow soon!
LLM-powered AI agents are the next leap in applied AI, capable of reasoning and collaboration to achieve even complex, multi-step goals. Using new protocols like MCP and A2A, agents can use software tools, retrieve relevant knowledge, and adapt to feedback. This book guides you step by step in creating an AI agent from the ground up, with clear, detailed explanations you can follow to build your own custom assistants! [Read more]
7 chapters of this new MEAP are available now, with more to follow soon!
Agents turn LLMs into autonomous tools capable of executing on tasks and plans. Multi-agent systems use protocols like MCP and A2A to upgrade the power of a single AI agent with both a collaborative AI team. This book shows you how to build a complete, working system of agents. Each chapter builds a new stage of your system. [Read more]
4 chapters of this new MEAP are available now, with more to follow soon!
When DeepSeek started making waves in January 2025, it sounded too good to be true. How could a generative AI model get such incredible performance with such low training and operation costs? In this guide, you'll find out exactly how DeepSeek works by recreating a laptop-scale version of this cutting-edge model yourself! [Read more]
5 chapters of this new MEAP are available now, with more to follow soon!
LLM reasoning models have the power to tackle truly challenging problems that require finding the right path through multiple steps. This book demystifies how these complex models work with a simple philosophy: the best way to learn how something works is to build it yourself! You'll begin with a pre-trained LLM, adding and improving its reasoning capabilities in ways you can see, test, and understand. [Read more]
7 chapters of this MEAP are available now, with more to follow soon!
Go inside the AI black box to tinker with the internal systems that power generative AI. As you work through each key stage of LLM creation, you'll develop an in-depth understanding of how LLMs work, their limitations, and their customization methods. Your LLM can be developed on an ordinary laptop, and used as your own personal assistant. [Read more]
Generative AI applications can be complex to understand and create. This book strips away the low level geekery and shows you exactly how to build Q&A systems, agents, and other powerful AI applications using low code tools. Along the way, you'll gain an intuition for how generative AI works, including core components like large language models, retrieval augmented generation, model context protocol, and more. [Read more]
When you need a language model to respond accurately and quickly about a specific field of knowledge, the sprawling capacity of a LLM may hurt more than it helps. This book teaches you to build generative AI models optimized for specific fields. You'll learn to minimize the computational horsepower your models require, while keeping high–quality performance times and output. [Read more]
A plain-English guide to RAG. The book is easy to follow and packed with realistic Python code examples. It takes you concept-by-concept from your first steps with RAG to advanced approaches, exploring how tools like LangChain and Python libraries make RAG easy. And to make sure you really understand how RAG works, you'll build a complete system yourself—even if you're new to AI! [Read more]
In this series of liveProjects, you'll help out a local pet refuge by building an AI-powered chatbot that shares both the knowledge and the attitudes of that compassionate institution. Starting with prompt engineering and persona design, you'll progressively layer in curated search, multi-use case handling, and callback scheduling. You'll take advantage of OpenAI's API along with LangChain and LangGraph to create a custom assistant that can deliver 24/7 pet care guidance. [Read more]
Even when AI-generated applications seem to "just work," they're often hiding technical debt, lack of control, unverifiable logic, and other slow-burn bugs that can rot your codebase and hinder productivity. Vibe engineering introduces checks and guardrails tuned to identify and address the most common AI coding issues, from specification through production. Vibe engineering also shows you how LLMs, agents, and other AI-powered tools can help you test, improve, and deploy your applications more efficiently. [Read more]
5 chapters of this MEAP are available now, with more to follow soon!
A comprehensive guide to creating LLM-based apps that are faster and more accurate. It takes you from training to production and beyond into the ongoing maintenance of an LLM. In each chapter, you'll find in-depth code samples and hands-on projects—including building a RAG-powered chatbot and an agent created with LangChain. [Read more]
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