Databricks guide: testing ai_extract prompt changes against ground truth with MLflow
Overview
Databricks says teams improve ai_extract document extraction by evaluating each prompt or schema change against human-reviewed ground truth, not by patching single fields.
In a project extracting about 40 fields from partner order PDFs, a schema change to the revision field roughly halved that field's errors, but total errors rose 9%. The post recommends putting field-level rules in ai_extract schemas, document-level rules in global instructions, and storing prompts and schemas in the MLflow Prompt Registry.
Written by AI from the articles below · updated Oct 27, 2:36 PM ET
Check the sources:
Article timeline
The articles in this story. Times are ET.
- Databricks BlogOfficialDatabricks 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%. 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.
Heat Trend
- Current comparable heat
- 6
- Peak comparable heat
- 10Oct 27, 3:00 PM ET
- Change in past 24 hours
- –
The trend compares only continuously observed participants, so its scope may be smaller than the current heat total. Hover or tap for hourly heat; use arrow keys with a keyboard.