What’s in Health
Overview
Health at a glance: accuracy and confidence, accuracy over time, and your weakest fields.
Datasets
Manage the workflow’s dataset — add documents, edit ground truth, and tag files into sub-datasets.
Evaluations
Run evaluations, browse the run list, and compare accuracy across workflow versions.
Optimizer
Automatic, iterative improvement of a workflow against its dataset. Coming soon.
Datasets and evaluations cover Extract and Classify workflows, and are fully decoupled from production — dataset edits and evaluation runs never touch your production data.
The Overview
The Overview gives you a high-level read on workflow health:- Average confidence and average accuracy
- Accuracy and confidence over time
- The weakest fields — where errors concentrate
The quality loop
Health is built around a simple, repeatable loop:1
Build a dataset
Add representative documents and confirm their correct values as ground truth. See Building a dataset.
2
Evaluate
Run a workflow version over the dataset and read its accuracy. See Running evaluations.
3
Refine
Improve fields, instructions, or the pipeline based on where it’s failing.
4
Re-evaluate and compare
Run again on the same dataset. Because the dataset is fixed and each evaluation is immutable, the difference between two runs is real signal, not noise.
What’s next?
Building a dataset
Add documents and author ground truth.
Running evaluations
Score a version and compare across versions.
