Skip to main content
The Preset Metric Examples sample project is a pre-populated Galileo project designed to help you understand how out-of-the-box metrics behave on real-looking examples. This project includes curated evaluation examples (within Log Streams and Experiments) with metric scores and explanations so you can quickly compare high-scoring vs. low-scoring cases.
The fastest way to explore is to start from a metric page in the docs, then look for the corresponding examples inside Preset Metric Examples.

​
How it’s organized

  • Curated examples: You’ll find pre-populated data that demonstrates how metrics score different cases.
  • Drill-down friendly: Open rows to compare the input/output with the metric explanation side-by-side.
  • Designed for contrast: Use sorting and filtering to compare strong vs. weak examples for the same metric.

​
What to look for

  • Score distribution: Look at the range of scores across traces to calibrate what “good” and “bad” looks like for that metric.
  • Explanations: Open a handful of rows and read the metric explanation carefully — it’s often the quickest way to learn the rubric the judge is applying.
  • Edge cases: Pay special attention to traces that surprise you (high score when you expected low, or vice versa). These are the best starting points for refining prompts, tools, or evaluation criteria.
  • Metric interplay: Some failures show up across multiple metrics. Use the examples to learn when you should monitor a second metric alongside your primary one.

​
A quick tour

1

Pick one metric you care about

Start from the relevant metric documentation page, then jump into the corresponding examples in Preset Metric Examples.
2

Review the best and worst traces

Sort by the metric value and open a few of the highest-scoring and lowest-scoring rows.
3

Extract reusable patterns

Keep track of 2–3 patterns that correlate with strong scores (and 2–3 patterns that correlate with weak scores). These become concrete hypotheses you can test in your own app.
4

Apply it to your own Log Stream

Enable the same metric on your own Log Stream, then see whether the patterns you observed hold up on your real traffic.

​
Jump into metric documentation

Response Quality metrics

Explore metrics focused on answer quality and grounding.

Agentic AI metrics

Explore metrics for multi-step agents, tool use, and trajectories.

Safety and Compliance metrics

Explore metrics focused on harmful content and prompt attacks.

Text-to-SQL metrics

Explore metrics for query correctness, adherence, efficiency, and safety.

​
Next steps