Databricks shows how to improve ai_extract accuracy with MLflow Prompt Registry
AIDatabricks says document extraction improves when teams evaluate changes against human-reviewed ground truth rather than fixing single fields by hand. In a project extracting about 40 fields from partner order PDFs, a revision-field schema change roughly halved that field's errors but raised total errors 9%.
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Databricks Blog · databricks.comPublished · added here
Original title: From spot fixes to measured gains: improving document extraction accuracy with ai_extract and MLflow Prompt Registry