progressive-disclosure

name	progressive-disclosure
description	Revealing AI capability gradually to match user mental models.

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)