> ## Documentation Index
> Fetch the complete documentation index at: https://docs.anyformat.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Optimizing a workflow
> Automatic, iterative improvement of a workflow's field descriptions, measured against your dataset
The **Optimizer** rewrites a workflow's **field descriptions** — the instructions the extraction model reads for every field — and keeps a change only when it **measurably improves accuracy** against your [dataset](/guides/health/datasets). It automates the *refine* step of the [quality loop](/guides/health#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
The current version is evaluated against the dataset. This is **iteration 0** — everything after is judged against it.
The Optimizer studies where the previous results went wrong and writes improved field descriptions.
The proposed descriptions are run over the dataset, like a regular [evaluation](/guides/health/evaluations).
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.** It means your current descriptions already perform as well as the proposals — the promoted version then simply matches your baseline, and nothing effectively changes.
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## What it needs
* **An Extract step** — 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 can't start one. See [Building a dataset](/guides/health/datasets).
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 to get a feel for it.
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## Starting a run
Choose **Run optimization** and set:
* **Scope** — the full dataset (sub-dataset scoping is coming).
* **Metric** — overall accuracy.
* **Iterations** — how many propose-and-evaluate rounds to attempt, **1–10** (default **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:
| Column | What it shows |
| ------------ | ------------------------------- |
| **Run** | The run's start time |
| **Status** | In progress, done, or failed |
| **Accuracy** | Baseline → best accuracy so far |
| **Rounds** | Iterations completed vs planned |
| **Date** | When the run started |
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: fields that stay inaccurate after an optimization are the ones that need more (or more varied) dataset examples.
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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, so if you disagree with a rewrite, restore an earlier version and you're back where you started.
While a run is active, each iteration is appended as the workflow's latest version — so documents processed **during** the run may use in-progress descriptions. Prefer optimizing at a quiet moment for the workflow.
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## Asking Annie
[Annie](/concepts/annie) can drive the Optimizer conversationally: *"improve this workflow"* starts a run, *"how did the optimization go?"* reports accuracy movement and the changes that were kept, and *"list the optimizations"* shows the history — useful when you're already in a chat about the workflow's results.
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## Current limits
* A running optimization **can't be cancelled** — it runs to completion. Size your first runs small.
* Only **extraction field descriptions** are optimized — Classify, Split, and Validate steps aren't touched.
* Scope and metric are fixed for now: **full dataset**, **overall accuracy**.
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## What's next?
Add documents and author the ground truth an optimization measures against.
Score a version by hand and compare accuracy across versions.