> ## Documentation Index
> Fetch the complete documentation index at: https://docs.galileo.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Preset Metric Examples

> Explore curated Log Streams that show Galileo’s out-of-the-box metrics in action

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.

<Tip>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**.</Tip>

## 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

<Steps>
  <Step title="Pick one metric you care about">Start from the relevant metric documentation page, then jump into the corresponding examples in **Preset Metric Examples**.</Step>
  <Step title="Review the best and worst traces">Sort by the metric value and open a few of the highest-scoring and lowest-scoring rows.</Step>
  <Step title="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.</Step>
  <Step title="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.</Step>
</Steps>

## Jump into metric documentation

<CardGroup cols={2}>
  <Card title="RAG metrics" icon="message" horizontal href="/concepts/metrics/rag/rag-overview">
    Explore metrics focused on answer quality and grounding.
  </Card>

  <Card title="Agentic AI metrics" icon="arrows-rotate" horizontal href="/concepts/metrics/agentic/agentic-overview">
    Explore metrics for multi-step agents, tool use, and trajectories.
  </Card>

  <Card title="Safety and Compliance metrics" icon="shield-halved" horizontal href="/concepts/metrics/safety-and-compliance/safety-and-compliance-overview">
    Explore metrics focused on harmful content and prompt attacks.
  </Card>

  <Card title="Text-to-SQL metrics" icon="database" horizontal href="/concepts/metrics/text2sql/text2sql-overview">
    Explore metrics for query correctness, adherence, efficiency, and safety.
  </Card>
</CardGroup>

## Next steps

* Learn how to enable metrics on your own Log Streams: [Configure metrics](/concepts/logging/configure-metrics/configure-metrics)
* Browse all out-of-the-box metrics: [Metrics overview](/concepts/metrics/overview)
* Compare metrics and decide what to monitor: [Metric comparison](/concepts/metrics/metric-comparison)