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.
Start here
Core patterns for onboarding UX.
9 patterns
Interactive Tutorials
Step-by-step AI guides
Example Prompts Library
Curated prompt examples
AI Tips & Tricks
Contextual AI guidance
Progressive Feature Unlock
Gradual AI introduction
AI Personality Customization
Customize AI assistant personality
Use Case Wizard
Guided setup based on user goals
First Success Flow
Guaranteed first successful AI interaction
Learning Path Recommendations
Personalized learning paths
Onboarding Progress Tracking
Visual progress through onboarding
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 example prompts?
Wizards configure goals, constraints, and defaults up front. Example prompt libraries are grab-and-go; wizards are for users who don’t know which template 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, first-success flows, and learning-path recommendations scale to teams; pair with admin-visible limits so onboarding promises match policy.