> For the complete documentation index, see [llms.txt](https://docs.feathery.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.feathery.io/platform/document-intelligence/review-extractions.md).

# Review Extractions

You can review extractions that are run in the `AI` tab by clicking on the Extraction we just defined, and then the `Results` tab. You can view the data pulled from each document that the extraction was run on and the location in the document where the data was found. You can also edit the extracted data if you find errors before approving the final result if you have reviewers defined for your extraction.

<figure><img src="/files/JGXCiFL4RsKK2EHQbySx" alt=""><figcaption></figcaption></figure>

## Configure Reviewers

If you don't set reviewers for your Extraction, runs will be auto-approved and subsequent actions (e.g. routing data) will be automatically triggered.

You can set reviewers for your Extraction in the configuration screen. The available reviewers are members of your Feathery team. Any configured reviewers will receive an email notification each time there's an available extraction to for them to review.

<figure><img src="/files/I1GPDnU9CKTVkbAnxjfh" alt="" width="375"><figcaption></figcaption></figure>

Reviewers will have access to an approval button on pending extraction runs to confirm the accuracy of the data.

<figure><img src="/files/R20Ot58dculJvaGyG7LT" alt="" width="375"><figcaption></figcaption></figure>

## Confidence Scores

Interaction with extracted values will affect the confidence score of the run and the values you vote on. Users are encouraged to approve/reject values through the UI and impact confidence scores. More details on this feature and how to enable it can be found [here](#confidence-scores)


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# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://docs.feathery.io/platform/document-intelligence/review-extractions.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.