---
title: "Annotate Traces"
sidebarTitle: "Overview"
---

<Frame caption="Applying the scientific method to building AI products - By Eugene Yan">
  <img src="https://storage.googleapis.com/arize-phoenix-assets/assets/images/phoenix-docs-images/d88cc26e-image.jpeg" />
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Annotating traces is a crucial aspect of evaluating and improving your LLM-based applications. By systematically recording qualitative or quantitative feedback on specific interactions or entire conversation flows, you can:

1. Track performance over time

2. Identify areas for improvement

3. Compare different model versions or prompts

4. Gather data for fine-tuning or retraining

5. Provide stakeholders with concrete metrics on system effectiveness

Phoenix allows you to annotate traces through the Client, the REST API, or the UI.

## Guides

* To learn how to configure annotations and to annotate through the UI, see [Annotating in the UI](/docs/phoenix/tracing/how-to-tracing/feedback-and-annotations/annotating-in-the-ui)

* To learn how to add human labels to your traces, either manually or programmatically, see [Annotating via the Client](/docs/phoenix/tracing/how-to-tracing/feedback-and-annotations/capture-feedback)

* To learn how to evaluate traces captured in Phoenix, see [Running Evals on Traces](/docs/phoenix/tracing/how-to-tracing/feedback-and-annotations/evaluating-phoenix-traces)

* To learn how to upload your own evaluation labels into Phoenix, see [Log Evaluation Results](/docs/phoenix/tracing/how-to-tracing/feedback-and-annotations/llm-evaluations)

For more background on the concept of annotations, see [Annotations](/docs/phoenix/tracing/llm-traces/how-to-annotate-traces)

<Frame caption="Adding manual annotations to traces">
  <img src="https://storage.googleapis.com/arize-assets/phoenix/assets/images/annotation_flow.gif" />
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