Premium skill
Extract structured data you can defend
Production structured-output pipelines on the AI SDK: schemas designed from downstream needs, bounded validation and repair, confidence routing to human review, and per-field accuracy on a golden document set.
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Use this when
- 01An invoice pipeline that refuses to write until the schema parses and totals balance.
- 02A team that thought extraction was 90 percent accurate until per-field metrics showed tax ids failing.
- 03A contract review UI that fills fields as partial objects stream, then captures corrections as golden cases.
- 04A 100k document month where batching and eval-gated cheaper models cut cost without regressing high-risk fields.
What you get
- Schemas from the consumer
- Fields come from what downstream reads, with absence and normalization agreed first.
- Repair with a cap
- Schema validation is the write gate. Exhausted repairs go to a dead letter.
- Per-field accuracy
- Golden sets report precision and recall per field, not one blended score.
- Routes low confidence
- Thresholds come from error cost. Weak values go to review, not production writes.
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