Build a Structured Data Extraction Pipeline

Why it matters

Deciding which extractions a person must see is only as sound as the signals behind the routing. This exercise builds those signals and then tests whether they hold across document types.

What to practise

Define an extraction schema with required, optional and nullable fields and an "other" enum value with a detail string, and confirm the model returns null rather than inventing a value that is absent from the source. Add a validation-retry loop, and record which failures a retry fixes (format) and which it cannot (missing information). Run the documents through the Message Batches API, find failures by custom_id, resubmit them changed, for example chunked, and check the total time against the SLA. Finally, have the model emit field-level confidence, route low-confidence fields to review, and measure accuracy by document type and field. The exercise uses tool_use for structured output; output_config.format serves the same role.

How it fits

Routing on confidence is 5.3.3’s decision, and it is sound only once accuracy per field proves the scores are calibrated. Keep policy triggers above them. The review volume this produces is the demand that 5.3.4 budgets, including sampling high-confidence output. Reviewer corrections become the evaluation cases that 5.3.5 builds.