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Definition

The conciseness test evaluates how brief and to-the-point the generated answer is while still being complete and informative. This metric is based on the Ragas aspect critique for conciseness.

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Taxonomy

  • Task types: LLM.
  • Availability: and .

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Why it matters

  • Conciseness ensures that your LLM generates responses that are appropriately brief without unnecessary verbosity.
  • This metric helps identify when your model produces overly lengthy or repetitive responses that could frustrate users.
  • It’s particularly important for applications with space constraints, mobile interfaces, or when quick, direct answers are preferred.

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Required columns

To compute this metric, your dataset must contain the following columns:
  • Input: The question or prompt given to the LLM
  • Outputs: The generated answer/response from your LLM
This metric relies on an LLM evaluator judging your submission. 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

If you are writing a tests.json, here are a few valid configurations for the conciseness test: 
[
  {
    "name": "Conciseness above 0.7",
    "description": "Ensure that generated responses are appropriately concise with a score above 0.7",
    "type": "performance",
    "subtype": "metricThreshold",
    "thresholds": [
      {
        "insightName": "metrics",
        "insightParameters": null,
        "measurement": "conciseness",
        "operator": ">",
        "value": 0.7
      }
    ],
    "subpopulationFilters": null,
    "mode": "development",
    "usesValidationDataset": true,
    "usesTrainingDataset": false,
    "usesMlModel": false,
    "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
  }
]