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

# Tracking User Feedback

When building LLM applications, it quickly becomes highly useful and important to track user feedback on the result of your LLM workflow.

Doing that with OpenLLMetry is easy. First, make sure you [associate your LLM workflow with unique identifiers](/openllmetry/tracing/association).

Then, [create an Annotation Task](https://app.traceloop.com/annotation-tasks) within Traceloop to collect user feedback as annotations.
The annotation task should include:

* The [entity](/openllmetry/tracing/association) you want to collect feedback for (e.g., `chat_id`)
* Tags you want to track (e.g., `score`, `feedback_text`)

You can log user feedback by calling our Python SDK or TypeScript SDK.
All feedback must follow the structure defined in your annotation task.

For example, to implement thumbs-up/thumbs-down feedback, create an annotation task with a tag named `is_helpful` that accepts the values `thumbs-up` and `thumbs-down`.

<Note>
  The entity you report feedback for must match the one defined in your annotation
  task and association property.
</Note>

<CodeGroup>
  ```python Python theme={null}
  from traceloop.sdk import Traceloop

  traceloop_client = Traceloop.get()
  traceloop_client.user_feedback.create(
      "your-annotation-task",
      "12345",
      {"is_helpful": "thumbs-up"},
  )
  ```

  ```js Typescript theme={null}
  const client = traceloop.getClient();
  await client.userFeedback.create({
      annotationTask: "your-annotation-task",
      entity: {
          id: "12345",
      },
      tags: {
          is_helpful: "thumbs-up",
      },
  });
  ```
</CodeGroup>