What are scores and metrics and when should I use each?

Last updated: April 13, 2025

Scores vs Metrics

When logging evaluation data in your pipeline, you have two options:

Type

Use Case

Value Range

Scores

For normalized quality measurements that will be visualized and analyzed across runs

Must be between 0 and 1

Metrics

For raw measurements like counts, lengths, or unbounded values

Any numeric value

How to Log Metrics

You can log metrics using the span.log() method with the metrics parameter:

span.log(metrics={
    "citation_count": 5,
    "response_length": 256
})

Note: While metrics can be logged with any numeric value, aggregation and visualization of custom metrics is not currently supported in the platform.

How to Log Scores

Scores should be used when you want to track normalized quality measurements that can be compared across runs. Always normalize scores to be between 0 and 1 before logging:

span.log(scores={
    "relevance": 0.85,
    "accuracy": 0.92
})