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

# Evaluation Parameters

> Data fields available to metric evaluators during scoring

When creating or configuring a metric, you select which parameters are relevant for your evaluation. These parameters are made available to the evaluator during scoring.

* For **AI Evaluation**, the selected parameters are automatically prepended to your `judge_prompt`.
* For **Human Evaluation**, they determine which data fields are displayed to annotators.
* For **Self-Hosted** metrics, evaluation parameters do not apply.

<Warning>
  Only select parameters that your evaluation criteria actually reference. Including unnecessary parameters can reduce evaluation quality by adding noise to the evaluator's context.
</Warning>

## Parameter Reference

<table>
  <thead className="text-left">
    <tr>
      <th>Parameter</th>
      <th>Description</th>
      <th>Availability</th>
    </tr>
  </thead>

  <tbody>
    <tr>
      <td>**input**</td>
      <td>The prompt or query sent to the model.</td>
      <td>Accuracy, Security & Safety, and Behavior</td>
    </tr>

    <tr>
      <td>**actual\_output**</td>
      <td>The actual output generated by the model.</td>
      <td>Accuracy, Security & Safety, and Behavior</td>
    </tr>

    <tr>
      <td>**expected\_output**</td>
      <td>The ideal answer for the given input.</td>
      <td>Accuracy and Security & Safety</td>
    </tr>

    <tr>
      <td>**context**</td>
      <td>Additional background information provided to the model alongside the input.</td>
      <td>All metrics</td>
    </tr>

    <tr>
      <td>**retrieval\_context**</td>
      <td>The context retrieved by your RAG system before sending the user query to your LLM. Derived from your `RETRIEVER` trace spans; you can also supply it directly and it is stored as a `RETRIEVER` trace, not a directly-readable field.</td>
      <td>Accuracy, Security & Safety, and Behavior</td>
    </tr>

    <tr>
      <td>**traces**</td>
      <td>Execution traces from the agent, including tool calls, LLM invocations, and other internal operations.</td>
      <td>All metrics</td>
    </tr>

    <tr>
      <td>**expected\_tools**</td>
      <td>List of tools expected to be used by the agent to accomplish the task.</td>
      <td>All metrics</td>
    </tr>

    <tr>
      <td>**tools\_used**</td>
      <td>List of tools actually used by the agent during execution (automatically inferred from traces).</td>
      <td>All metrics</td>
    </tr>

    <tr>
      <td>**product\_description**</td>
      <td>The description of the product.</td>
      <td>All metrics</td>
    </tr>

    <tr>
      <td>**product\_capabilities**</td>
      <td>The capabilities of the product.</td>
      <td>All metrics</td>
    </tr>

    <tr>
      <td>**product\_inabilities**</td>
      <td>The product's known inabilities or restrictions.</td>
      <td>All metrics</td>
    </tr>

    <tr>
      <td>**product\_security\_boundaries** *(deprecated)*</td>
      <td>The security boundaries of the product. Deprecated — no longer accepted for new metrics. Use `product_capabilities` and `product_inabilities` instead.</td>
      <td>N/A (rejected by API for new metrics)</td>
    </tr>

    <tr>
      <td>**user\_persona**</td>
      <td>Information about the user interacting with the agent.</td>
      <td>Behavior tests</td>
    </tr>

    <tr>
      <td>**goal**</td>
      <td>The user's objective in the conversation.</td>
      <td>Behavior tests</td>
    </tr>

    <tr>
      <td>**scenario**</td>
      <td>The context or situation for the conversation.</td>
      <td>Behavior tests</td>
    </tr>

    <tr>
      <td>**stopping\_criterias**</td>
      <td>List of criteria that define when a conversation should end.</td>
      <td>Behavior tests</td>
    </tr>

    <tr>
      <td>**conversation\_turns**</td>
      <td>All turns in a conversation, including user and assistant messages.</td>
      <td>Behavior tests (Human Evaluation only)</td>
    </tr>
  </tbody>
</table>

## Troubleshooting skipped evaluations

When an evaluation runs against a session that does not have all the data the metric needs, Galtea marks the evaluation as **SKIPPED** instead of producing a misleading score. The evaluation's error field describes which parameters are missing, grouped by where you provide them.

The categorized error message looks like this:

```
Metric "Contextual Relevancy" requires data that is not available.

Missing from product settings:
  • product_description - Fill in the "Description" field in your product settings.

Missing from test case:
  • expected_output - Provide an "Expected Output" value on your test cases.

Missing trace data:
  • retrieval_context - "retrieval_context" is derived from RETRIEVER trace spans. Configure tracing so your product emits retrieval steps — see the tracing setup docs.
  • traces - This metric needs tracing data. Configure your product to send traces — see the tracing setup docs.

Learn more: https://docs.galtea.ai/concepts/metric/evaluation-parameters#troubleshooting-skipped-evaluations
```

Each section points to a different place to look:

<table>
  <thead className="text-left">
    <tr>
      <th>Section</th>
      <th>Where to fix it</th>
    </tr>
  </thead>

  <tbody>
    <tr>
      <td>**Missing from product settings**</td>
      <td>Open the product and fill in the missing field (Description, Capabilities, or Inabilities). Capabilities and Inabilities can also be derived from the product's Specifications.</td>
    </tr>

    <tr>
      <td>**Missing from test case**</td>
      <td>Edit the test case and provide the missing value. Some fields (Goal, User Persona, Scenario, Stopping Criteria) are only populated on SCENARIOS-type test cases. If your test cases use a different type, use a metric that does not require these parameters.</td>
    </tr>

    <tr>
      <td>**Missing from endpoint connection's output mapping**</td>
      <td>Edit the endpoint connection and add the missing key to the output mapping. `actual_output` is extracted from the `output` key. See [Templates & Mapping](/concepts/product/endpoint-connection-configuration) for the JSONPath syntax used in mapping values.</td>
    </tr>

    <tr>
      <td>**Missing trace data**</td>
      <td>The metric needs trace-derived data (`traces`, `tools_used`, or `retrieval_context`). Configure your product to send traces — see the [Tracing Agent Operations](/sdk/tutorials/tracing-agent-operations) tutorial for setup, and the [Trace concept page](/concepts/product/version/session/trace) for what gets captured. `tools_used` (from `TOOL` spans) and `retrieval_context` (from `RETRIEVER` spans) are automatically extracted from your trace data; you do not provide them directly.</td>
    </tr>

    <tr>
      <td>**Missing inference data**</td>
      <td>The session has no inference results yet. Run the metric against a session that contains at least one conversation turn.</td>
    </tr>
  </tbody>
</table>

Once you have provided the missing data, re-run the evaluation against the same session.

## Related

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    Browse all available metrics and create custom ones.
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