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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

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Taxonomy

  • Task types: LLM.
  • Availability: and .
  • Evaluation level: session.
  • Polarity: higher score = better. 0 = completely irrelevant turns, 1 = all turns highly relevant.

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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.

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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.
This metric relies on an LLM evaluator. On Openlayer you can configure the underlying LLM used to compute it. Check out the OpenAI or Anthropic integration guides for details.

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Test configuration examples

[
  {
    "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
  }
]