> For the complete documentation index, see [llms.txt](https://docs.raga.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.raga.ai/ragaai-catalyst/ragaai-metric-library/additional-metrics/guardrails/factual-consistency.md).

# Factual Consistency

Ensure AI outputs stay factually consistent. Detect contradictions and improve trustworthiness.

This scanner checks if the provided content disagrees with a specific statement or question, this test ensures language model results are accurate and logical.

**Parameters**:

`data:`

* `prompt` (str): Prompt given to the model
* `response` (str): Reponse given by the model, which will be checked for factual consistency

`arguments:`

* `threshold` (float, optional): Threshold to determine if the response is factually correct. Default is 0.5.
* `use_onnx` (bool, optional): Whether to use onnx model to check for factual consistency.

**Interpretation**:

Higher score represents more logical and factually correct model response. The test passes if the model response is correct.

#### Code Example:

```python
prompt = "Angela Merkel is a politician in Germany and leader of the CDU."
response = "This text is about economics"

evaluator.add_test(
    test_names=["factual_consistency_guardrail"],
    data={'prompt':prompt,
          'response': response,
    },
    arguments={'model': 'gpt-4', 'threshold': 1.0},
).run()

evaluator.print_results()

```