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

# Chunk Utilization

> Measures how effectively a language model leverages information from the provided context to produce a coherent and contextually appropriate output.

<CodeGroup>
  ```python Python theme={null}
  result = evaluator.evaluate(
      eval_templates="chunk_utilization",
      inputs={
          "context": [
              "Paris is the capital and largest city of France.",
              "France is a country in Western Europe.",
              "Paris is known for its art museums and fashion districts."
          ],
          "output": "According to the provided information, Paris is the capital city of France. It is a major European city and a global center for art, fashion, and culture.",
          "input": "What is the capital of France?"
      },
      model_name="turing_flash"
  )

  print(result.eval_results[0].output)
  print(result.eval_results[0].reason)
  ```

  ```typescript JS/TS theme={null}
  import { Evaluator, Templates } from "@future-agi/ai-evaluation";

  const evaluator = new Evaluator();

  const result = await evaluator.evaluate(
    "chunk_utilization",
    {
      context: [
        "Paris is the capital and largest city of France.",
        "France is a country in Western Europe.",
        "Paris is known for its art museums and fashion districts."
      ],
      output: "According to the provided information, Paris is the capital city of France. It is a major European city and a global center for art, fashion, and culture.",
      input: "What is the capital of France?"
    },
    {
      modelName: "turing_flash",
    }
  );

  console.log(result);
  ```
</CodeGroup>

| **Input** |                    |                            |                                                  |
| --------- | ------------------ | -------------------------- | ------------------------------------------------ |
|           | **Required Input** | **Type**                   | **Description**                                  |
|           | `context`          | `string` or `list[string]` | The contextual information provided to the model |
|           | `output`           | `string`                   | The response generated by the language model     |

| **Output** |            |                                                                                             |
| ---------- | ---------- | ------------------------------------------------------------------------------------------- |
|            | **Field**  | **Description**                                                                             |
|            | **Score**  | Returns a numeric score, where higher values indicate more effective utilization of context |
|            | **Reason** | Provides a detailed explanation of the evaluation                                           |

***

## What to Do When Chunk Utilization Score is Low

* Ensure that the context provided is relevant and sufficiently detailed for the model to utilise effectively.
* Modify the input prompt to better guide the model in using the context. Clearer instructions may help the model understand how to incorporate the context into its response.
* If the model consistently fails to use context, it may require retraining or fine-tuning with more examples that emphasise the importance of context utilization.

***

## Differentiating Chunk Utilization with [Chunk Attribution](/future-agi/get-started/evaluation/builtin-evals/chunk-attribution)

Chunk Attribution assesses whether the model acknowledges and references the provided context at all, yielding a binary result: Pass if the context is used, or Fail if it is not. In contrast, Chunk Utilization evaluates how effectively the model incorporates that context into its response, producing a score that reflects the depth of its reliance on the information. While Attribution checks if the context was used, Utilization measures how well it was used to generate a meaningful and informed output.