> For the complete documentation index, see [llms.txt](https://docs.pullrequest.com/customer-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.pullrequest.com/customer-documentation/assign-code-review-to-hackerone-code-network/rating-reviews.md).
# Rating Reviews
We aim to help developers and teams at the highest level.
In an ongoing effort to ensure we provide the very best experience for our customers, we encourage code authors to submit ratings and provide feedback for the code reviews they receive from our developer network.
{% hint style="info" %}
**Rating reviews helps us ensure you're matched with reviewers from our network who are a great fit for your team.**
{% endhint %}
## **How to rate & provide feedback for a code review**
Every code review summary includes a footer with links to indicate if the review feedback provided was helpful or not. All you have to do is click one of the links.
In addition to direct feedback through ratings, we also utilize automatic sentiment detection. We use both responses in threads and emojis to make an assessment on how the review is received. The best case scenario for our code authors and customer teams is to respond to our feedback in one of these ways.
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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.pullrequest.com/customer-documentation/assign-code-review-to-hackerone-code-network/rating-reviews.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
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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.