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Definition

The session conversation completeness test evaluates whether a multi-turn conversation reached proper resolution with all topics adequately addressed. An LLM-as-a-judge reads the full conversation and scores it against four criteria:
  • The user’s initial request was fully addressed
  • All follow-up questions were answered
  • No topics were left unresolved or only partially addressed
  • The session reached a clear final response rather than trailing off

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Taxonomy

  • Task types: LLM.
  • Availability: and .
  • Evaluation level: session.
  • Polarity: higher score = better. 0 = completely incomplete, 1 = fully resolved.

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Why it matters

  • Incomplete conversations often signal unresolved issues that will resurface as repeat sessions, support tickets, or churn.
  • Complementary to Session goal achievement. The two overlap — both can penalize a session where the user’s request wasn’t addressed — but completeness also checks that every follow-up and sub-topic received an answer, not only the primary objective.

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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 completeness above 0.7",
    "description": "Ensure conversations reach a clean end state",
    "type": "performance",
    "subtype": "sessionConversationCompleteness",
    "thresholds": [
      {
        "insightName": "sessionConversationCompleteness",
        "measurement": "meanScore",
        "operator": ">=",
        "value": 0.7
      }
    ],
    "subpopulationFilters": null,
    "mode": "monitoring",
    "usesProductionData": true,
    "evaluationWindow": 3600,
    "delayWindow": 0
  }
]