CCA-F: Claude Certified Architect — Foundations

You are developing a structured-data extraction system with Claude. The system pulls information from unstructured documents, validates its output with JavaScript Object Notation (JSON) schemas, and maintains high accuracy. It must gracefully handle edge cases and integrate with downstream systems.

The system sends documents with extraction confidence below 85% for human review. A quarterly audit finds that 12% of high-confidence extractions (≥85%) also contain errors, in which the model identifies plausible but incorrect values. Sources of error differ: comparison tables that display competitor specifications, appendices that refer to other product variants, and ambiguous wording that the model misunderstands. You need a sustainable method to identify these high-confidence errors and determine over time whether improvements reduce the error rate.

What approach is most effective?