Overview
How might we design output history so people can trust and act on AI output?
When this pattern fits
- Perfect for iterative workflows, creative tools, and applications where users need to reference, compare, or restore previous AI-generated outputs.
When to skip or lighten it
- Ephemeral tools that must not retain outputs for privacy.
- Single-shot answers with no iteration loop.
- Histories so long they need search you have not built yet (cap and archive).
States
State model coming soon
Key UX elements
Key UX elements coming soon
Anti-patterns to avoid
Overwrite-only regenerate with no history.
History that cannot restore the full artifact state.
No retention policy for sensitive generations.
Restoring silently without marking which version is active.
How products use it
| Product | Implementation |
|---|---|
| Midjourney | Job history and remix from prior gens. |
| ChatGPT | Prior variants and canvas version affordances. |
| GitHub Copilot | Suggestion cycling with prior options. |
| Figma AI | Generation history on design explorations. |
Implementation
Copy this prompt to generate a production-ready implementation in Cursor, Claude Code, Lovable, or any AI coding agent.
Generate a production-ready implementation of the "Output History" AI interface design pattern.
Pattern Definition:Frequently asked questions
What should output history keep?
The artifact bytes or text, parameters used, and timestamp—enough to restore faithfully.
How is this different from conversation search?
Output history is versioning for one creative slot. Conversation search finds turns across chats.
How long to retain?
Match privacy needs; offer delete. Unlimited retention of sensitive gens is a liability.
Should restore create a branch?
Often yes—restoring an old draft as current while keeping newer ones in history.