Designing Human-Centered AI Interfaces

A few years ago, generative AI was a novelty. Today it sits inside the products we use every day. The systems we ship now are probabilistic, which means the same input does not always produce the same output. That single shift changes how users decide what to trust, how they recover from mistakes, and how much control they feel they have.

This book is a practical, design-first guide to building AI products that people can understand, trust, and direct. It covers foundations and mental models for probabilistic systems, followed by a library of interaction patterns for inputs, outputs, explainability, steerability, recommendations, memory, agents, multimodal experiences, and high-stakes domains like healthcare and finance. It also explores practical approaches to delivery, including prototyping with live models, scaling patterns through a design system, handing off to engineering, and where the field is heading next. Case studies are drawn from Spotify, GitHub Copilot, Perplexity, and other widely used products.

By the end, you will have the frameworks, checklists, and handoff templates to design, prototype, and evaluate AI features with confidence, collaborate well with engineering and data science, and ship products that are not only technically advanced but also ethical, accessible, and centered on human needs.

WHAT YOU WILL LEARN
● Master practical patterns for prompts, feedback, and explainability.
● Design steerable, interruptible UX that keeps humans in control.
● Visualize uncertainty and turn AI errors into trustworthy moments.
● Apply evaluation rubrics for usefulness, accuracy, safety, and delight.
● Build citations, provenance, and transparency directly into the interface.
● Scale AI patterns through design systems with consistent governance.
● Hand off to engineering with data contracts and guardrails.
● Prototype, test, and measure AI features using live models.

WHO THIS BOOK IS FOR
This book is for product designers, UX professionals, UI designers, HCI researchers, and design leaders building AI-powered products. It is also useful for product managers seeking a design-oriented view of AI and front-end engineers collaborating with design teams. Familiarity with digital product design is recommended; a machine learning background is not required.