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“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
