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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)
Input
Required InputTypeDescription
contextstring or list[string]The contextual information provided to the model
outputstringThe response generated by the language model
Output
FieldDescription
ScoreReturns a numeric score, where higher values indicate more effective utilization of context
ReasonProvides a detailed explanation of the evaluation

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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.

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Differentiating Chunk Utilization with 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.
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