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:

1. Surface layer: The most obvious, lowest-risk capabilities. Users discover these immediately.
2. Intermediate layer: More powerful features revealed through tooltips, suggestions, or first-use prompts.
3. 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)