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

# Session turn relevancy

> Learn how to use the session turn relevancy test to evaluate whether each turn is relevant to the conversation

## Definition

The session turn relevancy test evaluates whether **each turn is relevant** to the
ongoing conversation. An LLM-as-a-judge reads the full session and scores it
against four criteria:

* Each response directly addresses the user's question or request
* Responses avoid irrelevant information or padding
* Off-topic tangents are handled appropriately (redirected rather than indulged)
* The conversation stays focused on the thread the user is pursuing

## Taxonomy

* **Task types**: LLM.
* **Availability**: <Tooltip tip="Continuously evaluate your models and datasets as you iterate on their versions.">development</Tooltip>
  and <Tooltip tip="Monitor a model in production, measure its health, check for drifts and set up alerts.">monitoring</Tooltip>.
* **Evaluation level**: session.
* **Polarity**: higher score = better. `0` = completely irrelevant turns, `1` = all
  turns highly relevant.

## Why it matters

* Even assistants that answer factually correctly sometimes respond to adjacent but
  not directly-requested questions — a subtle quality degradation.
* Turn-level relevancy aggregated across a session catches patterns where the
  assistant consistently half-misunderstands the user.

## Required columns

* **Input**: The user's message in each turn.
* **Output**: The assistant's response in each turn.
* **Session ID**: Groups turns belonging to the same conversation.
* **Timestamp**: Used to reconstruct turn order within a session.

<Note>
  This metric relies on an LLM evaluator. On Openlayer you can configure the
  underlying LLM used to compute it. Check out the
  [OpenAI](/integrations/openai#openai-llm-evaluator) or
  [Anthropic](/integrations/anthropic#anthropic-llm-evaluator) integration
  guides for details.
</Note>

## Test configuration examples

<CodeGroup>
  ```json Monitoring theme={null}
  [
    {
      "name": "Session turn relevancy above 0.7",
      "description": "Ensure each turn is relevant to the user's question",
      "type": "performance",
      "subtype": "sessionTurnRelevancy",
      "thresholds": [
        {
          "insightName": "sessionTurnRelevancy",
          "measurement": "meanScore",
          "operator": ">=",
          "value": 0.7
        }
      ],
      "subpopulationFilters": null,
      "mode": "monitoring",
      "usesProductionData": true,
      "evaluationWindow": 3600,
      "delayWindow": 0
    }
  ]
  ```
</CodeGroup>

## Related

* [Answer relevancy](/tests/catalog/answer-relevancy) — trace-level relevancy metric (Ragas).