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

# Correctness

The Correctness Evaluator is a specialized tool designed to assess the accuracy of responses by comparing them against expected or ground truth responses. It uses an LLM (Large Language Model) as a judge to determine how well an actual response matches the expected response.

## Purpose

The Correctness Evaluator is particularly useful when you need to:

* Verify the factual accuracy of responses
* Ensure responses align with expected answers
* Detect contradictions or misinformation
* Evaluate the semantic similarity between responses

## How It Works

The evaluator uses a 5-point scale to rate responses:

* **Score: 1 (Direct Contradiction)**: The actual response directly contradicts the expected response
* **Score: 2 (Partial Contradiction)**: Contains some similar facts but also has direct contradictions
* **Score: 3 (Similar but Not Equivalent)**: Not contradictory but not equivalent
* **Score: 4 (Partial Equivalence)**: Some information is equivalent but not all
* **Score: 5 (Fully Equivalent)**: Both answers are equivalent

## Usage Example

```python theme={null}
import asyncio

from trusttest.evaluation_contexts import ExpectedResponseContext
from trusttest.evaluators import CorrectnessEvaluator


async def evaluate():
    evaluator = CorrectnessEvaluator()
    result = await evaluator.evaluate(
        response="What is the capital of Osona?",
        context=ExpectedResponseContext(
            expected_response="The capital of Osona is Vic."
        )
    )
    print(result)

if __name__ == "__main__":
    asyncio.run(evaluate())
```

The evaluator returns a tuple containing:

* A score (1-5) indicating the level of correctness
* A list of explanations for the given score

## When to Use

Use the Correctness Evaluator when you need to:

* Validate factual accuracy in QA systems
* Check response quality in chatbots
* Ensure consistency in information retrieval systems
* Evaluate the reliability of AI-generated content
* Test the accuracy of automated responses