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Definition

The session context retention test evaluates whether the assistant maintains and correctly uses context across the turns of a conversation. An LLM-as-a-judge reads the full session and scores it against four criteria:
  • Remembers facts and preferences established in prior turns
  • Builds upon previously established context rather than starting fresh each turn
  • Avoids asking for information the user has already provided
  • Doesn’t contradict information given earlier in the session

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Taxonomy

  • Task types: LLM.
  • Availability: and .
  • Evaluation level: session.
  • Polarity: higher score = better. 0 = no context retention, 1 = perfect context retention.

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Why it matters

  • Context-retention failures are a primary driver of user frustration in multi-turn assistants — especially re-asking for information already supplied.

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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 context retention above 0.7",
    "description": "Ensure the assistant maintains context across session turns",
    "type": "performance",
    "subtype": "sessionContextRetention",
    "thresholds": [
      {
        "insightName": "sessionContextRetention",
        "measurement": "meanScore",
        "operator": ">=",
        "value": 0.7
      }
    ],
    "subpopulationFilters": null,
    "mode": "monitoring",
    "usesProductionData": true,
    "evaluationWindow": 3600,
    "delayWindow": 0
  }
]