> ## Documentation Index
> Fetch the complete documentation index at: https://docs.eigenpal.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Evaluation

Use evaluation APIs when you want to manage datasets, run examples, start
experiments, and inspect evaluator scores from Python.

## Dataset examples

```python theme={null}
examples = client.automations.examples.list("workflows.extract-invoice")

example = client.automations.examples.create(
    "workflows.extract-invoice",
    {
        "name": "acme-invoice",
        "input": {"language": "en"},
        "expected": {"vendor": "Acme"},
    },
)
```

Examples contain input, expected JSON output, expected files, metadata, and
optional overrides. For file-heavy datasets, prefer archive import/export so the
folder layout stays portable.

Dataset archives use the canonical `examples/<name>/input.json` layout with
file references such as `{ "$file": "input/contract.pdf" }`. Legacy archives
with `manifest.json`, `input/arguments.json`, `expected/output.json`, or
`expected/error.json` are rejected; export a fresh ZIP before re-importing.

## Experiments

```python theme={null}
experiment = client.automations.experiments.create(
    "workflows.extract-invoice",
    {"examples": [example["id"]]},
)

detail = client.automations.experiments.get(
    "workflows.extract-invoice",
    experiment["id"],
)
```

An experiment runs dataset examples and records automated evaluator scores.
Older CLI docs may call the same id a `batchId`; API and SDK methods call it an
`experimentId`.

## Scores vs reviews

Evaluator `score` values are automated results. Run review `verdict` values are
human review decisions (`correct`, `incorrect`, or Nit). Use run review endpoints
when humans correct or promote a real run into the dataset. See
[Reviews](/concepts/reviews) for the review model.