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

# LLM Judge Metric

> Evaluate AI voice agent calls using natural language criteria with LLM-powered judging

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<CopyPageButton />

LLM Judge Metrics allow you to evaluate your AI voice agent calls using natural language descriptions. Instead of writing code, you simply describe what constitutes success in plain English, and the system automatically evaluates each call against your criteria. This makes it easy to create custom evaluations without programming knowledge.

<Note>
  For the canonical reference of `{{...}}` variables you can use in your metric prompts — and which are available in **Simulation** vs. **Observability** — see [Metric Variables](/documentation/key-concepts/metrics/metric-variables).
</Note>

### What You Can Evaluate

LLM Judge Metrics are ideal for evaluating qualitative aspects of conversations that require understanding context and nuance:

* **Workflow Compliance:** Check if agents followed specific steps or procedures
* **Communication Quality:** Assess tone, clarity, professionalism, or empathy
* **Information Accuracy:** Verify agents provided correct information or asked required questions
* **Customer Handling:** Evaluate objection handling, de-escalation, or problem resolution
* **Policy Adherence:** Ensure agents stayed within company policies and guidelines
* **Call Outcomes:** Determine if desired outcomes were achieved (bookings, resolutions, etc.)

### Benefits

* **No Coding Required:** Write evaluations in natural language, no programming skills needed
* **Flexible & Adaptable:** Easily modify criteria by updating your metric description
* **Context-Aware:** Understands conversational context, not just keyword matching
* **Dynamic Variables:** Use call-specific data (customer info, metadata) in your evaluations

### Creating LLM Judge Metrics

Navigate to the **Metrics** section and select **Create Metric**.

<Frame>
  <img src="https://mintcdn.com/vocera/Lzc22l97eeqPcAlN/images/metric/create-metric.png?fit=max&auto=format&n=Lzc22l97eeqPcAlN&q=85&s=0f64953a50269df226af876d2b4a8e6e" width="1571" height="1446" data-path="images/metric/create-metric.png" />
</Frame>

1. **Name & Type:** Give your metric a descriptive name (e.g., `Correct End Call by Main Agent`).
2. **Description (The Prompt):** Write a natural language description of what constitutes success. This is what the LLM Judge will use to evaluate calls.

<Tip>
  Use context variables to make the metric dynamic. For example, use `{{metadata.instructions}}` to reference specific scenario steps the agent was supposed to follow.

  You will see a list of context variables in the dashboard when creating a metric. See [Metric Variables](/documentation/key-concepts/metrics/metric-variables) for a complete list.
</Tip>

**Example Description:**

```
Check if the Main Agent ended the call only after all steps in
{{metadata.instructions}} were completed by the Testing Agent.
```

#### Set Triggers

Define when the metric should run under the **Evaluation Trigger** section.

* **Always:** Runs on every call (default).
* **Custom:** Use logic to run metrics only in specific scenarios. You can write a trigger prompt in natural language, or write Python code that decides when the metric should run. Trigger code receives the same [`data`](/documentation/key-concepts/metrics/python-metric#evaluation-trigger-custom-code) dictionary as a Python metric and sets `_result` (a bool — `True` to run, `False` to skip) and `_explanation`. See [Evaluation Trigger (Custom Code)](/documentation/key-concepts/metrics/python-metric#evaluation-trigger-custom-code) for the contract and an example.

### Testing Your Metrics

Before saving, validate your logic immediately within the builder.

<Frame>
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</Frame>

<Steps>
  <Step title="Click Test Metric">
    Navigate to the test section within the metric builder.
  </Step>

  <Step title="Select Call IDs">
    Select a few past **Call IDs** from the list to test against.
  </Step>

  <Step title="Run the Test">
    Run the test to see if the metric passes/fails as expected on historical data.
  </Step>

  <Step title="Create Metric">
    If satisfied with the results, click **Create Metric** to save.
  </Step>
</Steps>

## Audio Evaluation

When calling LLM Judge metrics from Python code, you can set `audio=True` to have the judge analyze the actual voice recording instead of (or in addition to) the transcript text. This is useful for evaluating speech delivery, pacing, tone, and other audio properties that the transcript alone cannot capture.

```python theme={null}
response = evaluate_llm_judge_metric(
    data,
    api_key,
    description="Did the agent speak clearly and at an appropriate pace?",
    eval_type="binary",
    audio=True,
    audio_start_time=5.2,   # optional: clip start in seconds
    audio_end_time=12.8,    # optional: clip end in seconds
)
```

See [Python Metric — Audio-Based Analysis](/documentation/key-concepts/metrics/python-metric#audio-based-analysis) for the full pattern.

## Related Documentation

* [Metric Variables](/documentation/key-concepts/metrics/metric-variables) - Variables you can use in metric descriptions
* [Creating Good Metrics](/documentation/guides/creating-good-metric) - Complete guide for building high-fidelity metrics
* [Python Metric](/documentation/key-concepts/metrics/python-metric) - Write custom evaluation logic in Python, including audio-based evaluation
* [Pre-defined Metrics](/documentation/key-concepts/metrics/pre-defined-metrics) - Generic metrics provided by Cekura