Annotate Traces
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:
Track performance over time
Identify areas for improvement
Compare different model versions or prompts
Gather data for fine-tuning or retraining
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
To learn how to add human labels to your traces, either manually or programmatically, see Annotating via the Client
To learn how to evaluate traces captured in Phoenix, see Running Evals on Traces
To learn how to upload your own evaluation labels into Phoenix, see Log Evaluation Results
For more background on the concept of annotations, see Annotations
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