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

# FTLResponseFaithfulness

> Evaluator to assess response faithfulness using Fiddler Centor Models.

Evaluator to assess response faithfulness using Fiddler Centor Models.

The FTLResponseFaithfulness evaluator uses Fiddler Centor Models to evaluate
how faithful an LLM response is to the provided context. This evaluator helps ensure
that responses accurately reflect the information in the source context and don't
contain hallucinated or fabricated information.

Key Features:

* **Faithfulness Assessment**: Evaluates how well the response reflects the context
* **Probability-Based Scoring**: Returns probability scores (0.0-1.0) for faithfulness
* **Context-Response Alignment**: Compares response against provided context
* **Faithfulness (Centor Model)**: Uses the Centor Model for Faithfulness, Fiddler's proprietary faithfulness evaluation model
* **Hallucination Detection**: Identifies responses that go beyond the context

Faithfulness Categories Evaluated:

* **faithful\_prob**: Probability that the response is faithful to the context

Use Cases:

* **RAG Systems**: Ensuring responses stay grounded in retrieved context
* **Document Q\&A**: Verifying answers are based on provided documents
* **Fact-Checking**: Validating that responses don't contain fabricated information
* **Content Validation**: Ensuring responses accurately reflect source material
* **Hallucination Detection**: Identifying responses that go beyond the context

Scoring Logic:
The faithfulness score represents the probability that the response is faithful to the context:

* **0.0-0.3**: Low faithfulness (likely contains hallucinated information)
* **0.3-0.7**: Medium faithfulness (some information may not be grounded)
* **0.7-1.0**: High faithfulness (response accurately reflects context)

## Parameters

* **response** (*str*) – The LLM response to evaluate for faithfulness.
* **context** (*str*) – The source context that the response should be faithful to.
* **score\_name\_prefix** (*str* *|* *None*)
* **score\_fn\_kwargs\_mapping** (*ScoreFnKwargsMappingType* *|* *None*)

## Returns

<ResponseField type="list[Score]">
  A list of Score objects containing:

  * name: The faithfulness category name ("faithful\_prob")
  * evaluator\_name: "FTLResponseFaithfulness"
  * value: Probability score (0.0-1.0) for faithfulness
</ResponseField>

## Raises

**ValueError** – If the response or context is empty or None.

## Example

```python theme={null}
from fiddler_evals.evaluators import FTLResponseFaithfulness
evaluator = FTLResponseFaithfulness()
```

```python theme={null}
# Faithful response
context = "The capital of France is Paris. It is located in northern Europe."
response = "Paris is the capital of France."
scores = evaluator.score(response=response, context=context)
for score in scores:

    print(f"{score.name}: {score.value}")

# faithful_prob: 0.95

# Unfaithful response with hallucination
context = "The capital of France is Paris."
response = "The capital of France is Paris, and it has a population of 2.1 million people."
scores = evaluator.score(response=response, context=context)
for score in scores:

    print(f"{score.name}: {score.value}")

# faithful_prob: 0.65 (population info not in context)

# Highly unfaithful response
context = "The capital of France is Paris."
response = "The capital of France is London."
scores = evaluator.score(response=response, context=context)
for score in scores:

    print(f"{score.name}: {score.value}")

# faithful_prob: 0.05

# Filter based on faithfulness threshold
faithful_score = next(s for s in scores if s.name == "faithful_prob")
if faithful_score.value < 0.7:

    print("Response flagged as potentially unfaithful")
```

<Info>
  This evaluator is designed for response faithfulness assessment and should be used
  in conjunction with other evaluation metrics for comprehensive response quality
  assessment. The probability scores should be interpreted in context and combined
  with other quality measures for robust response validation.
</Info>

## name *= 'ftl\_response\_faithfulness'*

## score()

Score the faithfulness of a response to its context.

### Parameters

<ParamField path="response" type="str" required={true}>
  The LLM response to evaluate for faithfulness.
</ParamField>

<ParamField path="context" type="str" required={true}>
  The source context that the response should be faithful to.
</ParamField>

### Returns

<ResponseField type="Score">
  A Score object for faithfulness probability.
</ResponseField>