Название: Grokking AI Applications: An illustrated guide for programmers and other curious people (Final Release)
Автор: Andrea De Mauro
Издательство: Manning Publications
Год: 2026
Страниц: 283
Язык: английский
Формат: epub
Размер: 84.1 MB

“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.

The focus is on the durable parts of AI development. Prompt engineering, retrieval-augmented generation (RAG), agentic structures, tool design, and orchestration are the “primitives” of any AI application, and they will outlast any specific library or framework. By mastering them, you give yourself a foundation that stays relevant as the technology around you keeps changing.

To keep the journey enjoyable, fast, and accessible, the tutorials are built using two low-code environments: Langflow and KNIME. The concepts and patterns you’ll learn translate naturally to any tool or codebase you choose to work in later. By the last page, you’ll have a portfolio of working apps you can adapt to your own needs and a clear mental model for designing many more.

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

Who should read this book
The book is written for two primary categories of readers:
• The first group includes individuals with computer literacy and a keen interest in AI, but without specialized AI coding skills. They may be web programmers, data enthusiasts, or Python coders without experience in developing AI applications. Typical roles include web developers, AI hobbyists, aspiring data scientists, and tech professionals looking to expand their skill set into AI development quickly.
• The second group includes nontechnical readers who want to understand how LLM chain design and orchestration work, so they can apply AI in their jobs. They aren’t looking to switch careers; instead, they want to partner more effectively with AI in their existing professions. They also want firsthand experience with the potentialof AI so they can guide their technical colleagues on what they need. This group includes managers and other non-IT staff in various industries who view AI as a strategic asset in their work and seek hands-on experience partnering with machine intelligence.
For the Python developers among you, every chapter from chapter 2 onward includes a “What if we coded it?” section that translates the chapter’s tutorial into a fully commented Python script, so you can immediately reapply the concepts in your own code and pick up the relevant libraries along the way.

about the authors
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.