Debugging LLM requests
Sometimes you might see an LLM output that doesn’t look right. In such cases, you can quickly debug the output by opening the model event in the Playground and iterating on your prompt to resolve the issue. You can open a model event in your trace in the Playground by clicking theOpen in Playground
button on the top right of your trace.
Expected time: 1-2 minutes
Annotating outputs
It’s important to manually review your LLM outputs even if you use automated evaluators, in order to judge whether the outputs meet your specific criteria. Teams often involve domain experts in this process. To do so, you can define human evaluation criteria in theEvaluators
tab and use the UI to score outputs and provide comments.

Annotation Queues: You can create an annotation queue by simply using the trace and span-level filters in the
Data Store
. Use Completions
tab if you’re looking to navigate between LLM requests, and Sessions
tab if you’re looking to navigate between traces. You can use keyboard shortcuts like ⬆️ and ⬇️ to navigate across rows.Curating datasets
With the UI, you can curate datasets for your overall session, completions or any particular step of your pipeline. In the following example, we will do so for the overall session. You can simply add a filter forevent_name
or go to the Completions
tab to curate model requests.
Expected time: 1-2 minutes
Steps:
Sharing traces
Sharing a trace is as simple as clicking the Share button on the top right of your trace.