Grokking AI Applications: An Illustrated Guide for Programmers and Other Curious People
暫譯: 深入理解 AI 應用：程式設計師與其他好奇者的插圖指南
de Mauro, Andrea
商品描述
Get the eBook free when you register your print book at Manning. "Clear, practical, and focused on getting you from basics to real-world applications fast."
--Michele Pacifico, Nestlé Grokking AI Applications: An illustrated guide for programmers and other curious people shows you how to create AI applications like chatbots, agents, and systems customized to use your own data--even if you've never built one before! Supporting his explanations with graphics and using concrete examples and hands-on implementations, author Andrea De Mauro gets you thinking about system design and mental models rather than boilerplate code and syntax debugging. You'll quickly focus on the right level: how to manage prompts, data, and application components to get the results you want. You'll start with a bare-bones chatbot, as you learn how to create good prompts and improve accuracy using Retrieval Augmented Generation (RAG). To help your applications live in the real world, the book discusses how to connect them to document collections, structured databases, and CRM tools. You'll build on this foundation, creating your own agents and equipping them with tools for data analysis and complex reasoning. Reviewer Julien Pohie of Thoughtworks commented, "The emphasis on multi-agent orchestration aligns perfectly with modern engineering best practices." Step-by-step guidance, rich diagrams, and workflow summaries help you stay oriented, even through advanced material. To emphasize conceptual and design thinking, you'll start each project using the low-code development tool Langflow. For readers who want to go deeper, every example also includes fully-annotated Python implementations. You'll see the code, libraries, APIs, and data behind each application, along with practical advice on how to deploy and monitor your applications. Compact by design, this fast-paced book includes eight fully illustrated chapters that build intuition through engaging, memorable, and relatable real-world scenarios. As you read, you'll experience a shift from AI as the familiar conversational chatbot to AI as an essential part of your application and, further, to the design of multi-agentic teams with specialized roles and supervised coordination. And you'll enjoy every page of the journey! What's inside - Low-code examples using Langflow and KNIME
- Core mechanics of RAG, agentic structures, and prompt engineering
- Practical AI projects including chatbots and reasoning agents About the reader For readers of all levels who want to create AI applications. The use of visual, low-code tools makes it accessible even to those unfamiliar with Python. About the author Andrea De Mauro has two decades of experience leading Data and AI teams in global organizations like Vodafone and Procter & Gamble. He teaches Applied AI at Luiss University. Illustrator Ines Schweigert earned her PhD at the Institute for Imaging and Computer Vision at RWTH Aachen University, Germany. Table of Contents 1 Peeking inside the black box
2 Building your first GenAI app
3 Writing the right prompt
4 Augmenting LLMs with your own documents
5 Expanding GenAI capabilities with tools and agents
6 Implementing agentic structures
7 Integrating GenAI in your data ecosystem
8 Deploying and orchestrating GenAI apps
--Michele Pacifico, Nestlé Grokking AI Applications: An illustrated guide for programmers and other curious people shows you how to create AI applications like chatbots, agents, and systems customized to use your own data--even if you've never built one before! Supporting his explanations with graphics and using concrete examples and hands-on implementations, author Andrea De Mauro gets you thinking about system design and mental models rather than boilerplate code and syntax debugging. You'll quickly focus on the right level: how to manage prompts, data, and application components to get the results you want. You'll start with a bare-bones chatbot, as you learn how to create good prompts and improve accuracy using Retrieval Augmented Generation (RAG). To help your applications live in the real world, the book discusses how to connect them to document collections, structured databases, and CRM tools. You'll build on this foundation, creating your own agents and equipping them with tools for data analysis and complex reasoning. Reviewer Julien Pohie of Thoughtworks commented, "The emphasis on multi-agent orchestration aligns perfectly with modern engineering best practices." Step-by-step guidance, rich diagrams, and workflow summaries help you stay oriented, even through advanced material. To emphasize conceptual and design thinking, you'll start each project using the low-code development tool Langflow. For readers who want to go deeper, every example also includes fully-annotated Python implementations. You'll see the code, libraries, APIs, and data behind each application, along with practical advice on how to deploy and monitor your applications. Compact by design, this fast-paced book includes eight fully illustrated chapters that build intuition through engaging, memorable, and relatable real-world scenarios. As you read, you'll experience a shift from AI as the familiar conversational chatbot to AI as an essential part of your application and, further, to the design of multi-agentic teams with specialized roles and supervised coordination. And you'll enjoy every page of the journey! What's inside - Low-code examples using Langflow and KNIME
- Core mechanics of RAG, agentic structures, and prompt engineering
- Practical AI projects including chatbots and reasoning agents About the reader For readers of all levels who want to create AI applications. The use of visual, low-code tools makes it accessible even to those unfamiliar with Python. About the author Andrea De Mauro has two decades of experience leading Data and AI teams in global organizations like Vodafone and Procter & Gamble. He teaches Applied AI at Luiss University. Illustrator Ines Schweigert earned her PhD at the Institute for Imaging and Computer Vision at RWTH Aachen University, Germany. Table of Contents 1 Peeking inside the black box
2 Building your first GenAI app
3 Writing the right prompt
4 Augmenting LLMs with your own documents
5 Expanding GenAI capabilities with tools and agents
6 Implementing agentic structures
7 Integrating GenAI in your data ecosystem
8 Deploying and orchestrating GenAI apps
商品描述(中文翻譯)
在Manning註冊您的印刷書籍時可免費獲得電子書。
「清晰、實用，專注於快速從基礎到實際應用。」--Michele Pacifico, Nestlé Grokking AI Applications: An illustrated guide for programmers and other curious people 會教您如何創建像聊天機器人、代理和系統這樣的AI應用，並根據您自己的數據進行自定義——即使您從未構建過這樣的應用！作者 Andrea De Mauro 通過圖形支持他的解釋，並使用具體的例子和實踐實現，讓您思考系統設計和心理模型，而不是模板代碼和語法調試。您將迅速專注於正確的層面：如何管理提示、數據和應用組件，以獲得所需的結果。 您將從一個簡單的聊天機器人開始，學習如何創建良好的提示並使用檢索增強生成（Retrieval Augmented Generation, RAG）來提高準確性。為了幫助您的應用在現實世界中運行，本書討論了如何將它們連接到文檔集合、結構化數據庫和CRM工具。您將在此基礎上構建，創建自己的代理並為其配備數據分析和複雜推理的工具。評論者 Julien Pohie 來自Thoughtworks評論道：「對多代理協調的強調與現代工程最佳實踐完美契合。」 逐步指導、豐富的圖表和工作流程摘要幫助您保持方向感，即使在高級材料中也是如此。為了強調概念和設計思維，您將使用低代碼開發工具Langflow開始每個項目。對於希望深入了解的讀者，每個例子還包括完整註釋的Python實現。您將看到每個應用背後的代碼、庫、API和數據，以及有關如何部署和監控您的應用的實用建議。 這本設計緊湊的快節奏書籍包含八個完全插圖的章節，通過引人入勝、令人難忘且易於理解的現實場景來建立直覺。在閱讀過程中，您將體驗到AI從熟悉的對話聊天機器人轉變為您應用的必要部分，進一步轉變為具有專業角色和監督協調的多代理團隊的設計。 您將享受這段旅程的每一頁！ 內容包括 - 使用Langflow和KNIME的低代碼示例
- RAG的核心機制、代理結構和提示工程
- 實用的AI項目，包括聊天機器人和推理代理 讀者對象 適合所有級別的讀者，想要創建AI應用。使用視覺化的低代碼工具使其即使對不熟悉Python的人也能輕鬆上手。 關於作者 Andrea De Mauro 在Vodafone和Procter & Gamble等全球組織中擁有二十年的數據和AI團隊領導經驗。他在Luiss大學教授應用AI。插畫家 Ines Schweigert 在德國亞琛工業大學的成像與計算機視覺研究所獲得博士學位。 目錄 1 窺探黑箱
2 建立您的第一個GenAI應用
3 編寫正確的提示
4 使用您自己的文檔增強LLMs
5 使用工具和代理擴展GenAI能力
6 實施代理結構
7 將GenAI整合到您的數據生態系統中
8 部署和協調GenAI應用
作者簡介
Andrea De Mauro has two decades of experience leading Data and AI teams in global organizations. A seasoned executive advisor and fractional CDO, Andrea has held leadership roles at Vodafone and Procter & Gamble, shaping data-driven strategies at scale. He teaches Marketing Analytics and Applied AI at Luiss University in Rome and other top European universities. Recognized in CDO Magazine's Forty Under 40 list, he is also a prolific researcher, exploring the fundamental forces behind big data as a phenomenon and the transformative impact of AI and data analytics on businesses and society.
作者簡介(中文翻譯)
安德烈亞·德·毛羅 擁有二十年的經驗，領導全球組織中的數據和人工智慧團隊。作為一位資深的執行顧問和部分時間的首席數據官（CDO），安德烈亞曾在沃達豐（Vodafone）和寶潔（Procter & Gamble）擔任領導職位，制定大規模的數據驅動策略。他在羅馬的盧伊斯大學（Luiss University）及其他頂尖歐洲大學教授行銷分析和應用人工智慧。安德烈亞被《CDO Magazine》評選為「四十位40歲以下的傑出人物」之一，同時也是一位多產的研究者，探索大數據作為現象背後的基本力量，以及人工智慧和數據分析對企業和社會的變革性影響。
