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Every workflow has a Health tab. It is where you measure how well the workflow performs and drive that up over time. It gathers quality metrics, dataset management, and evaluations in one place, so you measure quality without leaving the workflow. The short version: the Overview shows how the workflow is doing right now. To measure quality deliberately, build a dataset of documents with known-correct answers and run evaluations against it. The dataset stays fixed, so two workflow versions compare cleanly.

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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’s field descriptions against its dataset.
Datasets and evaluations score a workflow’s extraction output, so a workflow needs an Extract node. Parse-only and split workflows are excluded. Both are decoupled from production. Dataset edits and evaluation runs never touch your production data.

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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
Analytics & Quality explains what each number means and how to use it.

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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 the fields, the instructions, or the nodes, guided by where the workflow fails. Or let the Optimizer rewrite the field descriptions for you.
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.

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What’s next?

Building a dataset

Add documents and author ground truth.

Running evaluations

Score a version and compare across versions.