Grokking AI Applications

Building with generative AI involves more than connecting a chatbot to a prompt. In Grokking AI Applications, Andrea De Mauro explains how to shape the systems around a large language model—adding knowledge, tools, data connections, and coordinated workflows to turn text generation into useful applications. Illustrated explanations and practical projects keep the focus on how the pieces fit together.

Start with the model, then build around it

The book begins by examining how large language models work and what makes GenAI programming different from conventional software development. From there, it guides readers through prompt design and the creation of an initial application, establishing concepts that recur throughout the later chapters.

Give applications more than a prompt

Subsequent chapters explore retrieval-augmented generation (RAG), which lets an application draw on supplied documents, and the use of tools and agents when an AI system needs to retrieve information or take action. The discussion then expands to agentic structures, including teams of specialized agents, and to integrating GenAI workflows with wider data systems.

Learn by building with Langflow and KNIME

Examples use the low-code platforms Langflow and KNIME to make application flows visible as they are assembled. Projects described in the contents include a chatbot, a travel agent, a copywriting team, and personalized email generation. The final chapter turns to deployment and orchestration, carrying the book’s progression from foundational ideas toward running complete applications.

A practical guide for curious builders 💻

Programmers and readers exploring how GenAI applications are designed will find a structured introduction to prompts, RAG, agents, data integration, and deployment. The illustrated, example-led approach makes the system design easier to follow while keeping the emphasis on transferable building blocks rather than one isolated use case.