Overview
How might we design batch input processing so people can trust and act on AI output?
When this pattern fits
- Ideal for data processing tools, bulk operations, and workflows where processing multiple inputs simultaneously improves efficiency and productivity.
When to skip or lighten it
- Interactive tasks needing immediate single-turn feedback.
- Batch size caps ignored on shared tenant quotas.
- Items that must be strictly ordered with cross-dependencies.
States
State model coming soon
Key UX elements
Key UX elements coming soon
Anti-patterns to avoid
Batch with no per-item error detail.
All-or-nothing export when half failed.
No estimate of total time or cost upfront.
Restart entire batch after one validation error.
How products use it
| Product | Implementation |
|---|---|
| Image processing tools | Multi-upload queues with per-file progress bars. |
| Data analysis platforms | CSV row batches through AI enrichment pipelines. |
| Document processors | Folder ingest with OCR and summary per doc. |
| Batch APIs | Job IDs, webhooks, and downloadable result bundles. |
Implementation
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Generate a production-ready implementation of the "Batch Input Processing" AI interface design pattern.
Pattern Definition:Frequently asked questions
What UI does batch processing need?
Upload list, queue order, status per item, aggregate progress, and download or retry failed rows.
Batch input vs batch processing queue?
Input processing is user-facing multi-submit. Processing queue is backend job orchestration with same UX patterns.
Parallel or serial?
Show policy: parallel for independent items; serial when rate limits require it.
Partial failure handling?
Export successes and a failure report with row IDs and error reasons.