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Evaluation Using Interface

Input:
  • Required:
    • context: - The contextual information provided to the model.
    • output: - The response generated by the language model.
  • Optional:
    • input: - The original query or instruction given to the model.
Output:
  • Result: - Passed / Failed
Interpretation:
  • Passed: signifies that the model acknowledged the context, which is a prerequisite for generating contextually grounded responses.
  • Failed: indicates a potential issue, such as the model ignoring the context, the context being entirely irrelevant, or the prompt not adequately instructing the model to use the context. This often points to problems in the retrieval or generation step of a RAG system.

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Evaluation Using Python SDK

Click here to learn how to setup evaluation using the Python SDK.
InputParameterTypeDescription
Requiredcontextstring or list[string]The contextual information provided to the model.
outputstringThe response generated by the language model.
OptionalinputstringThe original query or instruction given to the model.
OutputTypeDescription
ResultstringPassed / Failed
from fi.testcases import TestCase
from fi.evals.templates import ChunkAttribution

test_case = TestCase(
    input="What is the capital of France?",
    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.",
    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."
    ]
)

template = ChunkAttribution()

response = evaluator.evaluate(eval_templates=[template], inputs=[test_case], model_name="turing_flash")

print(f"Score: {response.eval_results[0].metrics[0].value}")
print(f"Reason: {response.eval_results[0].reason}")

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What to Do When Chunk Attribution Fails

  • Ensure that the context provided is relevant and sufficiently detailed for the model to utilise effectively. Irrelevant context might be ignored.
  • Modify the input prompt to explicitly guide the model to use the context. Clearer instructions (e.g., “Using the provided documents, answer…”) can help.
  • Check the retrieval mechanism: Is the correct context being retrieved and passed to the generation model?
  • If the model consistently fails to use context despite relevant information and clear prompts, it may require fine-tuning with examples that emphasize context utilization.

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Differentiating Chunk Attribution with Chunk Utilization

Chunk Attribution verifies whether the model references the provided context at all, focusing on its ability to acknowledge and use relevant information. It results in a binary outcome—either the context is used (Passed) or it is not (Failed). In contrast, Chunk Utilization measures how effectively the model integrates the context into its response, assigning a score (typically 0 to 1) that reflects the degree of reliance on the provided information. While Attribution confirms if context is considered, Utilization evaluates how much of it contributes to generating a well-informed response.