No Hype AI: AI Product Design

A decision framework for whether AI is the right solution — is the task ambiguous, does it benefit from personalization, is there tolerance for imperfect output, is there a fallback — with anti-patterns and success stories.

A pattern library of how AI appears in products — chat, inline suggestions, autonomous agents, and ambient intelligence — analysing when each works, when it fails, and what users expect.

Build appropriate trust — confidence indicators, source attribution, explainability, and user control — and design patterns that help users calibrate reliance without over-trusting.

AI will be wrong — design hallucination UX, graceful degradation, easy user corrections, and error-recovery flows for the happy path, the subtle error, and the catastrophic failure.

Great AI products improve through use — explicit, implicit, and structured feedback, and how to design feedback users actually provide by reducing friction and showing impact.

Deep personalization without the creepiness — building user models, adaptation strategies, progressive personalization, and the privacy-respecting boundaries that keep it helpful.

Evaluate AI features before building them — Wizard-of-Oz testing, prompt prototyping, progressive rollout, and AI-in-the-loop vs AI-in-the-lead validation for non-deterministic features.

Metrics built for AI — task completion, time-to-value, error-correction rate, adoption and retention, and satisfaction — plus setting baselines, handling non-determinism, and avoiding vanity metrics.

AI products can harm at scale — dark patterns to avoid, protecting user autonomy, informed consent about AI use, and equitable access — applied through a structured ethics review.

Learn from products that shipped and scaled — the problem solved, UX patterns chosen, error/trust handling, and feedback design — extracting transferable principles across categories.