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In order to improve your LLM application iteratively, it’s vital to collect feedback, annotate data during human review, as well as to establish an evaluation pipeline so that you can monitor your application. In Phoenix we capture this type of feedback in the form of annotations. Phoenix gives you the ability to annotate traces with feedback from the UI, your application, or wherever you would like to perform evaluation. Phoenix’s annotation model is simple yet powerful - given an entity such as a span that is collected, you can assign a label and/or a score to that entity.

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Next Steps

Annotation Concepts

Understand how annotations work in Phoenix

Run Evals on Traces

Automatically evaluate your traces with LLM judges

Annotate in the UI

Manually review and annotate traces

Log Annotations via Code

Programmatically add annotations from your app