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Definition

The session coherence test evaluates the logical flow and consistency of a multi-turn conversation. An LLM-as-a-judge reads the full conversation and scores it against four criteria:
  • Responses logically follow from the user’s messages
  • The overall trajectory of the conversation is easy to follow
  • Individual responses are well-structured and internally consistent
  • Transitions between topics feel smooth

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Taxonomy

  • Task types: LLM.
  • Availability: and .
  • Evaluation level: session.
  • Polarity: higher score = better. 0 = completely incoherent, 1 = perfectly coherent.

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Why it matters

  • Coherence is a distinct quality from correctness: an assistant can give factually correct answers that still feel disjointed or contradictory across turns.
  • Low coherence is a strong leading indicator of user dissatisfaction even when task outcomes look fine.

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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 coherence above 0.7",
    "description": "Ensure conversations maintain logical flow across turns",
    "type": "performance",
    "subtype": "sessionCoherence",
    "thresholds": [
      {
        "insightName": "sessionCoherence",
        "measurement": "meanScore",
        "operator": ">=",
        "value": 0.7
      }
    ],
    "subpopulationFilters": null,
    "mode": "monitoring",
    "usesProductionData": true,
    "evaluationWindow": 3600,
    "delayWindow": 0
  }
]