Onboarding AI UX patterns
Onboarding patterns reduce cold-start friction: guided wizards, progressive disclosure, tutorials, and empty states that teach what the AI can do.
Essential
Guide a first success with starters, wizards, and progressive unlock.
Frequently asked questions
What makes a good AI onboarding pattern?
Show capability with a narrow first success path, not a blank box. Combine examples, templates, or a wizard so users learn prompt shape, limits, and what the model can’t do.
Should onboarding be in-product or a separate tour?
Prefer contextual onboarding at the moment of need: empty states, tips near the composer, and progressive unlock, over one long modal tour users skip.
How is a use-case wizard different from prompt starters?
Wizards configure goals, constraints, and defaults up front. Prompt starters (empty-state cards, templates, or libraries) are grab-and-go; wizards help when users don’t know which starter fits.
When should I use progressive feature unlock?
When the product has many AI capabilities and novices would be overwhelmed. Reveal advanced tools after core success, not on day one.
How do I measure onboarding pattern success?
Track time-to-first-successful generation, repeat use of taught features, and drop-off on empty states. Good onboarding reduces “blank prompt” abandonment.
Which onboarding patterns work for enterprise rollouts?
Interactive tutorials, prompt starters with a clear first job, and learning-path recommendations scale to teams; pair with admin-visible limits so onboarding promises match policy.