Overview
How might we design predictive type so people can trust and act on AI output?
When this pattern fits
- Perfect for email clients, messaging applications, and text input tools where predictive completions accelerate writing and reduce typing effort.
When to skip or lighten it
- Secure fields (passwords, secrets) where prediction must never run.
- Legal or medical drafting that requires every character to be deliberate.
- Mobile keyboards where ghost text fights system autocomplete.
States
State model coming soon
Key UX elements
Key UX elements coming soon
Anti-patterns to avoid
Auto-inserting completions without an explicit accept gesture.
Predictions that leak other users’ data into ghost text.
No easy dismiss, forcing users to fight the suggestion.
Completions that ignore the document’s established style.
How products use it
| Product | Implementation |
|---|---|
| Gmail | Smart Compose ghost text in email drafts. |
| GitHub Copilot | Inline code ghost text accepted with Tab. |
| Notion | AI write-ahead suggestions in docs. |
| Grammarly | Inline completion and rewrite hints while typing. |
Implementation
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Generate a production-ready implementation of the "Predictive Type" AI interface design pattern.
Pattern Definition:Frequently asked questions
What is predictive type in AI UX?
Predictive type shows suggested continuation as ghost text; users accept with a key (often Tab) or keep typing to ignore it.
How is it different from smart autocomplete?
Predictive type continues natural prose or code at the caret. Smart autocomplete often ranks commands, mentions, or structured tokens.
What makes accept safe?
Require an explicit accept key, keep undo, and never submit forms on accept. Ghost text should never send messages by itself.
When should predictions pause?
In password fields, when the user is selecting text, and when network or privacy mode disables assistance.