Knowledge Notes

Product Design & UX ／ Other Product, Design and Learning Materials

Curated Highlights of Medium UX Articles (Multi-Topic: GenAI UX Patterns / Research & Experiment Methods / Mobile Trends / Inclusivity / Information Architecture / Company Cases)

A curated compilation of highlights from 102 Medium UX articles (59 topics) saved by Will, compressed across 7 major clusters into one note that can replace an entire bookmarks folder. ① GenAI/AI UX Patterns — 21 GenAI UX Patterns (augmentation vs automation / three tiers of automation / CoT display / expressing model confidence and limitations / guardrails), seven AI UI layouts (floating window / inline / infinite canvas / centered chat / left panel / right panel / grid), predictive UX (context management controls), five-step framework for designing AI products. ② Research & Experiment Methods — five-step A/B testing method (define success → identify bottlenecks → build hypotheses → prioritize → test, including empirical evidence from the Obama campaign's $57 million and Haiti disaster relief's $1.02 million), macro vs micro vs vanity metrics. ③ Mobile & Trends — nine 2026 mobile trends (predictive / Zero-UI / Agentic / spatial 3D / glassmorphism / microinteractions / adaptive / ethics / passwordless). ④ Inclusivity/Accessibility — 11 tactics (4.5:1 contrast ratio / multi-channel coding / external form labels / alt text / heading hierarchy / touch targets ≥7–10 mm² / keyboard accessibility), including empirical revenue evidence from Tesco/Virgin. ⑤ Information Architecture/Flows/Complexity — collaboration framework (centralized → decentralized shared Commons), how complexity accumulates (feature bloat / process stacking / technical debt + Pareto cuts + complexity budget), value proposition canvas, globalization ≠ translation (Chevrolet Nova / Domino's vs Kellogg's). ⑥ Company Cases — breakdown index for Airbnb/Calm/Canva/Duolingo/Spotify/Uber/Slack/Gmail and more. ⑦ Career/Soft Skills — three types of designers in 2026 (block-shaped generalists / ultra-deep specialists / standout new graduates), real UX competence in the AI era (restraint and judgment > drawing UI), six psychology principles for landing page conversion. For AI product PMs: Why / when to use / counterexamples come first. Highly intertextual with [[shape-of-ai-patterns]] (AI UX Pattern Library), [[ai-ux-interactions]] (Seven AI Interaction Paradigms), [[laws-of-ux]] (Laws of UX), [[nngroup-ux-principles]] (UX Golden Quotes), [[nngroup-information-architecture]], [[nngroup-persuasive-design]], and [[ux-metrics-roi]]; this compilation only adds the checklists, numbers, and cases they do not cover, and does not rewrite them.

AI UX User research AccessibilityEnglish edition

Product Design & UX ／ Other Product, Design and Learning Materials

Shape of AI AI Product UX Pattern Library (Seven Major Categories: Wayfinders/Inputs/Tuners/Identifiers/Governors/Trust/Dark Patterns + Design Guidelines)

A self-contained bilingual essence of Emily Campbell’s *Shape of AI* AI product UX pattern library (all 49 .md files fully read), reorganized into 36 specific patterns across 7 categories plus 1 set of overarching design guidelines, following a queryable structure of “Pattern Name → What Problem It Solves → How to Use It → Examples”. The seven categories are: ① Wayfinders (4 patterns: Follow up / Nudges / Suggestions / Templates) — solves the problem of new users not knowing how to use a blank input box; ② Inputs (8 patterns: Open input / Auto Fill / Inline action / Madlibs / Remix-Blend / Summary / Synthesis / Token layering) — feeds user intent and context to the AI; ③ Tuners (8 patterns: Filters / Parameters / Inpainting / Model management / Personal voice / Primary sources / References RAG / Workflows) — lets users fine-tune or restructure results; ④ Identifiers (6 patterns: Color scheme purple-green / Symbols icons ✨🔮🤖 / Name / Personality / Disclosure / Initial CTA) — distinguishes AI content from human content; ⑤ Governors (9 patterns: Citations / Controls stop/continue / Footprints / Prompt transparency / Regenerate / Sample response / Show the work / Token transparency / Variations) — preserves user agency and explainability while the AI works; ⑥ Trust indicators (3 patterns: Incognito / Memory management / Watermarks C2PA); ⑦ Dark matte hidden patterns (3 patterns: Data Ownership / Caveat disclaimer buck-passing / Rating) — gray-area designs that are questionable or erode trust. For PMs building AI chat or interactive narrative products: this is a quick reference manual for “which pattern to look up when you encounter interaction problem X next time”, with emphasis on Why / When to use / Counterexamples. It complements nngroup-ai-ux-design — the latter offers NNGroup’s conversational interface research perspective (6 types of conversation / no fluff / anthropomorphism / sycophancy), while this piece presents Shape of AI’s perspective as a “reusable UI pattern parts library”.

AI UX Interaction Patterns Trust DesignEnglish edition