# Databricks shows how to improve ai_extract accuracy with MLflow Prompt Registry

> Original title: From spot fixes to measured gains: improving document extraction accuracy with ai_extract and MLflow Prompt Registry

- Source: Databricks Blog
- Published: 2026-10-27T18:00:00.000Z
- Bad Influence: https://badinfluence.news/items/we7qiwb6y59lkvq6e1agv60fw
- Original: https://www.databricks.com/blog/spot-fixes-measured-gains-improving-document-extraction-accuracy-aiextract-and-mlflow-prompt

## Summary

Databricks 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%. The post recommends placing field rules in ai_extract schemas, document-level rules in global instructions, and storing prompts and schemas in MLflow Prompt Registry.