> ## Documentation Index
> Fetch the complete documentation index at: https://docs.openlayer.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Context utilization

> Learn how to use the context utilization test

## Definition

The context utilization test measures how effectively the LLM uses the provided context when generating its response. This metric evaluates whether the model is appropriately leveraging the available contextual information to produce better answers.

## Taxonomy

* **Task types**: LLM.
* **Availability**: <Tooltip tip="Continuously evaluate your models and datasets as you iterate on their versions.">development</Tooltip>
  and <Tooltip tip="Monitor a model in production, measure its health, check for drifts and set up alerts.">monitoring</Tooltip>.

## Why it matters

* Context utilization ensures that your LLM is effectively using the retrieved or provided context to improve its responses.
* This metric helps identify when your model is ignoring relevant context or not incorporating it appropriately into its answers.
* It's particularly important for RAG (Retrieval-Augmented Generation) systems where context should enhance the quality of generated responses.

## 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
* **Context**: The provided context or background information

<Note>
  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](/integrations/openai#openai-llm-evaluator) or
  [Anthropic](/integrations/anthropic#anthropic-llm-evaluator) integration
  guides for details.
</Note>

## Test configuration examples

If you are writing a `tests.json`, here are a few valid configurations for the context utilization test:

<CodeGroup>
  ```json Development theme={null}
  [
    {
      "name": "Context utilization above 0.7",
      "description": "Ensure that the LLM effectively uses provided context with a score above 0.7",
      "type": "performance",
      "subtype": "metricThreshold",
      "thresholds": [
        {
          "insightName": "metrics",
          "insightParameters": null,
          "measurement": "contextUtilization",
          "operator": ">",
          "value": 0.7
        }
      ],
      "subpopulationFilters": null,
      "mode": "development",
      "usesValidationDataset": true,
      "usesTrainingDataset": false,
      "usesMlModel": false,
      "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
    }
  ]
  ```

  ```json Monitoring theme={null}
  [
    {
      "name": "Context utilization above 0.7",
      "description": "Ensure that the LLM effectively uses provided context with a score above 0.7",
      "type": "performance",
      "subtype": "metricThreshold",
      "thresholds": [
        {
          "insightName": "metrics",
          "insightParameters": null,
          "measurement": "contextUtilization",
          "operator": ">",
          "value": 0.7
        }
      ],
      "subpopulationFilters": null,
      "mode": "monitoring",
      "usesProductionData": true,
      "evaluationWindow": 3600,
      "delayWindow": 0,
      "syncId": "b4dee7dc-4f15-48ca-a282-63e2c04e0689"
    }
  ]
  ```
</CodeGroup>

## Related

* [Ragas integration](/integrations/ragas) - Learn more about Ragas metrics.
* [Context relevancy test](/tests/catalog/context-relevancy) - Measure relevance of retrieved context.
* [Context recall test](/tests/catalog/context-recall) - Measure completeness of retrieved context.
* [Faithfulness test](/tests/catalog/faithfulness) - Evaluate factual consistency with context.
* [Aggregate metrics](/tests/performance/aggregate-metrics) - Overview of all available metrics.