Patterns for prompts, agent plans, transparency, approval, and AI output review.
Open in lab Source-backed UX patterns 298 complete entries 897 sources
Choose the right UX pattern before the interface hardens.
A practical decision system for product teams, designers, frontend engineers, and AI agents: search the problem, compare nearby patterns, inspect required states, and verify the source trail.
- 298
- pattern entries
- 298
- complete patterns
- 286
- comparisons
- 897
- evidence sources
Use it like a product decision system
The site is structured around the work a real team has to do before an interaction ships.
Review Use the checklist to define task fit, risk, required states, accessibility checks, and evidence before the choice becomes a ticket. Choose Start from the user problem, compare nearby alternatives, and avoid choosing a component by name alone. Inspect Open the lab to test states, keyboard behavior, failure modes, and implementation contracts before copying a pattern. Prove Trace guidance back to sources, verification dates, and source-backed claims when a pattern affects risk or trust.
Pattern families
Start from the user problem, not from a component name.
Patterns for comments, mentions, presence, feeds, handoffs, and shared work.
Open in labPatterns for touch, sensors, device permissions, haptics, camera, and offline use.
Open in labPatterns for tables, dashboards, charts, maps, timelines, and drill-down views.
Open in labPatterns for revealing detail, focusing attention, and keeping context stable.
Open in labPatterns for preventing mistakes, explaining failure, retrying, undoing, and restoring work.
Open in labPatterns for loading, progress, alerts, banners, notifications, and system confidence.
Open in labPatterns for forms, fields, validation, uploads, dates, addresses, payments, and account data.
Open in labPatterns for moving through pages, sections, steps, breadcrumbs, menus, and local hierarchy.
Open in labPatterns for defaults, settings, preferences, saved views, recommendations, and user control.
Open in labPatterns for search, filters, categories, suggestions, empty results, and saved retrieval.
Open in labPatterns for choosing values, commands, objects, options, ranges, and contextual actions.
Open in labPatterns for staged tasks, approvals, review, scheduling, checkout, and multi-step work.
Open in labPatterns for consent, warnings, privacy, security, permissions, reporting, and sensitive data.
Open in labHigh-stakes patterns to inspect first
These entries show the standard every page is moving toward: decision rule, quality bar, implementation contract, and evidence trail.
AI limitation onboarding
A user is introduced to an AI feature whose abilities, limits, data scope, uncertainty, or review needs are not obvious from the normal interface.
AI And Automation UX - 5 sourcesEmpty state
The product area can legitimately contain no user data.
Feedback, Status, And System State - 4 sourcesError state
A system or task failure blocks expected content or action.
Feedback, Status, And System State - 4 sourcesHuman approval gate
An AI agent, workflow, deployment, or automation is ready to perform a high-impact step and must pause for human authorization.
AI And Automation UX - 6 sourcesInline validation
A single field has a specific correctable problem.
Input And Data Entry - 2 sourcesSource grounding display
Users need answer-wide evidence coverage before trusting generated content.
AI And Automation UX - 7 sourcesOne practical pattern at a time.
The newsletter belongs after the catalog path: a concise pattern brief for teams that want sharper implementation and review language.
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