Progressive Disclosure
Users don't understand what AI can do. Progressive disclosure is how you reveal capabilities at the right pace — preventing both overwhelm and underuse.
The Mental Model Gap
Users arrive with mental models shaped by previous technology. They may:
- Treat the AI like a search engine (keyword queries)
- Treat it like a form (expecting rigid structure)
- Underestimate what it can do (asking for less than it offers)
- Overestimate what it can do (expecting perfection) Progressive disclosure bridges the gap between what users think the AI does and what it actually does.
Disclosure Strategies
- On-demand hints: Show capability suggestions contextually ("Did you know you can also ask me to...")
- Escalating examples: Start with simple use cases, reveal complex ones as the user gains confidence
- Feature graduation: Unlock advanced features after the user demonstrates comfort with basics
- Contextual teaching: When the user attempts something inefficiently, show a better approach
- Capability boundaries: Clearly communicate what the AI cannot do, not just what it can
Layered Capability Revelation
Structure capabilities in layers:
- Surface layer: The most obvious, lowest-risk capabilities. Users discover these immediately.
- Intermediate layer: More powerful features revealed through tooltips, suggestions, or first-use prompts.
- Power layer: Advanced capabilities for experienced users — available but not promoted.
Pacing
- Too fast: Users feel overwhelmed, ignore capabilities, or lose trust
- Too slow: Users get bored, think the product is limited, churn
- Just right: Each new capability feels like a natural next step
Design Artefacts
- Capability disclosure maps showing what's revealed when
- Mental model progression diagrams
- First-use experience flows with disclosure triggers
- Capability tier definitions (surface, intermediate, power)