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The Optimizer rewrites a workflow’s field descriptions, the instructions the extraction model reads for every field. It keeps a change only when that change measurably improves accuracy against your dataset. It automates the refine step of the quality loop: propose a change, evaluate it, keep it only if it helps. The Optimizer lives under Health → Optimizer.

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What a run does

1

Measure the baseline

The Optimizer evaluates the current version against the dataset. This is iteration 0. Everything after is judged against it.
2

Propose

The Optimizer studies where the previous results went wrong and writes improved field descriptions.
3

Evaluate

The Optimizer runs the proposed descriptions over the dataset, like a regular evaluation.
4

Keep only improvements

An iteration is accepted only if its accuracy beats the best result so far. Propose → evaluate repeats up to the number of iterations you chose.
When the run finishes, the best iteration is promoted automatically as a new workflow version. Each iteration is also recorded as a patch version, so the full trail stays visible in the workflow’s version history.
“No improvement found” is a valid outcome. Your current descriptions already perform as well as the proposals. The promoted version matches your baseline, and nothing changes.

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What it needs

  • An Extract node. The Optimizer improves extraction field descriptions, so parse-only and split workflows are excluded.
  • A dataset with ground truth. The run scores every proposal against your documents’ known-correct values, so a dataset without ground truth cannot start one. See Building a dataset.
Every iteration runs extractions over the whole dataset, which consumes credits. A run costs roughly dataset size × (iterations + 1) extractions, baseline included. Start with a small dataset and few iterations.

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Starting a run

Choose Run optimization and set:
  • Scope: the full dataset.
  • Metric: overall accuracy.
  • Iterations: how many propose-and-evaluate rounds to attempt, from 1 to 10. The default is 3.
A workflow allows one active optimization at a time. A second start is rejected until the current run finishes.

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Following a run

Runs appear in the Optimizer table. Status, accuracy, and rounds update live as the run progresses: Open a run for the full picture: an accuracy-per-iteration chart, the timeline of iterations, and each iteration’s proposed description changes. Open a single iteration to see its per-field accuracy, which fields improved, which still mismatch, and the exact description edits it tried. The per-field view doubles as a work list. A field that stays inaccurate after an optimization needs more dataset examples, or more varied ones.

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Versions and rolling back

The promoted result goes live automatically. It becomes the workflow’s latest version, with no separate review step. Every change is a normal version-history entry. If you disagree with a rewrite, restore an earlier version.
While a run is active, each iteration is appended as the workflow’s latest version. Documents processed during the run may use in-progress descriptions. Optimize at a quiet moment for the workflow.

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Asking annie

annie drives the Optimizer conversationally, which helps when you are already in a chat about the workflow’s results. “improve this workflow” starts a run. “how did the optimization go?” reports accuracy movement and the changes she kept. “list the optimizations” shows the history.

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Current limits

  • A running optimization cannot be cancelled. It runs to completion. Size your first runs small.
  • The Optimizer changes extraction field descriptions only. It never touches Classify, Split, or Validate nodes.
  • Scope and metric are fixed for now: full dataset, overall accuracy.

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What’s next?

Building a dataset

Add documents and author the ground truth an optimization measures against.

Running evaluations

Score a version by hand and compare accuracy across versions.