Overview
How might we design batch processing queue so people can trust and act on AI output?
When this pattern fits
- Ideal for data processing workflows, bulk operations, and applications where queuing multiple AI requests improves efficiency and resource utilization.
When to skip or lighten it
- Single interactive chat turns.
- Queues with no rate limit visibility on shared APIs.
- Jobs with hard ordering dependencies shown as parallel.
States
State model coming soon
Key UX elements
Key UX elements coming soon
Anti-patterns to avoid
Queue UI with no failed item retry.
Cancel all with no confirm on large batches.
No notification when background queue completes.
Estimated time wildly wrong with no update.
How products use it
| Product | Implementation |
|---|---|
| Image processing tools | Overnight upscale queues with email done. |
| Data analysis platforms | Batch inference jobs with job IDs. |
| API batch endpoints | OpenAI-style batch API status pages. |
| Automation tools | Run history with retry on failed steps. |
Implementation
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Pattern Definition:Frequently asked questions
Queue vs batch input processing?
Same user mental model: input is submit many; queue is track execution.
Priority controls?
Let users bump urgent rows and pause lower priority segments.
Background run?
Allow close tab with email or in-app notify on completion.
Cost display?
Show running total and estimate before start.