This is the prompt and workflow handbook for the first phase (Discover & Research) of Will’s "AI-Assisted UX Workflow". It covers 7 tasks in the discovery and research phase that can be accelerated by AI: ① Distill scattered project background into a professional "Project Background (PB) statement" ② Derive business goals × experience goals × plain-language driving logic from the background ③ Locate experience problems on old interfaces and quantify their metric impact ④ Rewrite vague problems into actionable problem statements + problem overview diagrams ⑤ Use MVP hypothesis/metric scripts for interview verbal delivery ⑥ Generate a fully structured product concept user research report (including simulated data + risk and objection signals) ⑦ Convert content into "chart prompts" for professional infographics. Each section provides reusable prompt skeletons + Why explanations + counterexamples + sample outputs. It also includes the underlying mental models (business outcome = traffic × conversion × cost, experience only acts on the behavioral layer, MVP dual hypotheses, four-dimensional demand scoring). It complements google-ux-research: that piece covers UX research methods themselves (how to conduct interviews/draw empathy maps/run usability tests), while this piece explains how to use AI to quickly turn these research outputs into deliverables suitable for reporting or inclusion in a portfolio. It is oriented toward non-technical PMs, with jargon explained in parentheses.
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.
The complete research craft collection from Google UX Certificate Course 2 (Empathize/Define) + Course 4 (Conduct UX Research). Covers 5-step interview SOP + 5 traits of research objectives/participants/questions + empathy map (Says/Thinks/Does/Feels) + persona vs user profile + problem statement (formula + 5W1H) + hypothesis statement (if-then / we believe) + 4-step value proposition + human factors engineering (WWI/WWII history) + design psychology (color/Von Restorff/serial position/Hick's Law/mental model/feedback loop + ethics) + 7 elements of a research plan + usability study (pros and cons of moderated vs unmoderated + best practices before/during/after + note-taking methods) + moderator/participant bias + affinity diagram analysis + patterns → themes + 5 criteria for strong insights + 7 presentation tips. Targeted at PMs, cross-referenced with Yitang research/user interview methodology.
Highlights from a large-scale Icon Association Ranking survey: 107 U.S. participants × 100 icons = 10,700 open-ended responses, covering the primary associated meaning, recognition rate percentage, and 95% confidence interval for each icon when presented alone, without text or context. Core finding: There is no such thing as a "Universal Icon" — no icon is consistently understood across all systems. Recognition rates fall into three tiers: 65–100% (high consensus, e.g., trash can = delete 98%, shopping cart = checkout 98%, pencil = edit 96%, envelope = mail 95%, circled question mark = help 91%, house = home 91%) / 50–64.9% (medium consensus, e.g., hamburger menu = menu only 59%, funnel = filter 53%) / 0–49.9% (weak consensus, e.g., share arrows, sparkles for AI, lifebuoy for help, AR cube). Coverage: survey methodology (two-stage testing: out-of-context vs. in-context) / three icon types (resemblance/metaphorical/arbitrary) and cognitive distance / 10 design principles (follow convention, single concept, consistent meaning, appropriate metaphor, etc.) + real-world counterexamples (Reddit wrench, TripIt star upgrade, Rippling heart for insurance, Lyft golf cart for shopping cart, DoorDash lifebuoy for help) / 8 factors affecting comprehension (consistency, interaction frequency, prior experience, proximity, text labels, orientation, placement, cultural background) / when text labels are mandatory / why emerging icons all fail (AI sparkles 0% recognition, heartbeat for data, lightning for charging 37%). Audience: product managers; jargon explained in plain language; every section includes Why + numbers + counterexamples.
A curated knowledge base of 22 essential pieces from Nielsen Norman Group on usability testing and usability heuristics, organized around two core axes: ① The 10 usability heuristics proposed by Jakob Nielsen in 1994 (used for design self-checks and heuristic evaluations): Visibility of System Status / Match Between System and the Real World / User Control and Freedom / Consistency and Standards (including internal + external consistency · Jakob's Law) / Error Prevention (prioritize high-risk scenarios) / Recognition Rather than Recall / Flexibility and Efficiency of Use (accelerators) / Aesthetic and Minimalist Design (signal-to-noise ratio) / Help Users Recognize, Diagnose, and Recover from Errors / Help and Documentation — each entry includes core principles, do/don't guidance, and counterexamples, plus application notes for complex enterprise applications (ArcMap/Power BI/Salesforce/Revit/Solidworks/Jitterbit); User Delight is built on a four-level pyramid (Aarron Walter) of functionality-reliability-usability-delight, covering surface delight vs deep delight plus the negativity bias effect. ② Usability testing methods: the golden rule that 5 users uncover 85% of problems and its exceptions (quantitative studies ≥20 users · card sorting ≥15 users · eye tracking 39 users · agile 2–3 users · empirical evidence from 83 studies), participant recruitment ($171 per participant · agency vs internal recruitment · incentives · 11% no-show rate · survey of 201 people), 7 steps for moderated remote testing, three techniques for task scenarios (realistic/actionable/no leading hints), Stepped Tasks research for discoverability and findability, Attitudinal vs Behavioral research, 4-step qualitative data analysis method (collect/examine/understand/validate), large measurement error in small samples (true value + measurement error · confidence intervals · statistical significance · do not report raw numbers alone), competitive usability evaluation (select 2–4 competitors · expert review vs user testing). Intended audience: product managers; terms are explained in industry language, with decision-oriented guidance.
Highlights from Will's Design Notes *UX Metrics and ROI* (based on the NN/g research report of the same name + a 5-edition case library). It focuses on the three things PMs need most: ① Metric system checklist — what each UX metric (task completion rate / time on task / success rate / conversion rate / retention / CLV / bounce rate…) looks like, how to calculate it, when to use it, and what its limitations are; how to interpret standardized questionnaires SUS (System Usability Scale, full score 100), NPS (Net Promoter Score = %Promoters − %Detractors), and CSAT; the five dimensions of Google's HEART framework (Happiness / Engagement / Adoption / Retention / Task success) as a scaffold for metric selection; what questions each of the four major data sources (surveys / quantitative usability testing / analytics tools / customer service and marketing data) answers. ② UX→ROI conversion methodology — improvement score formula (positive metric = post-change ÷ pre-change − 1; negative metric = pre-change ÷ post-change − 1), three-step method (select KPI → convert UX metrics to KPI by unit → report responsibly), how to convert to monetary value for four types of business models (e-commerce by sales volume / content sites by PV-based advertising / intranets by person-day wages / sites with clear target actions by conversion × average order value), retaining all exact figures including health insurance site saving 1.5 million yuan/year, HR tool saving $6,112 per month / 60% of work hours, financial subscription trial increasing monthly revenue by $20,000 from 76 to 187, and intranet ROI reaching 8/20/50 times the cost for companies with 1,000/10,000/100,000 employees; also presents Athenahealth's chained correlation model of "usability → satisfaction → willingness to recommend → retention and paid referrals", teaching PMs to connect soft metrics to revenue KPIs using statistical correlation. ③ Expectation management and reporting talking points — the "narrowing improvement" pattern that UX redesigns deliver an average 75% lift (95% confidence interval 8%~104%), and that this figure shrank from 247% to 75% from 2006 to 2020 (p=0.017), broken down by metric category (effectiveness 131% / efficiency 104% / adoption & retention 93% / engagement 87% / satisfaction 19%); 5 iron rules for reporting to bosses (estimates are not financial reports / compare against R&D costs / tell stories instead of piling tables / be transparent about assumptions / extend the timeline to 2–5 years) plus counterexamples. It complements *nngroup-analytics-metrics*: that piece covers NN/g's "analytics and measurement perspective" (vanity metrics / confidence intervals / statistical significance / sampling / triangulation / four-step ROI principle), while this piece is a "complete metric system checklist + improvement score math + multi-business-model ROI conversion recipes + empirical baseline magnitude data + reporting talking points"; overlapping content (four-step ROI / HEART / confidence intervals) is only cross-referenced and not rewritten. Target audience: product managers.
