# RAG evaluation

Scorable provides evaluators for *Retrieval Augmented Generation (RAG)* use cases, where you can give the context as part of the evaluated content.

## Hallucination Detection

One of the most useful evaluators in RAG settings is **Faithfulness** which detects claims that can not be deducted from the context, i.e., *hallucinations* in RAG setup.

Here is an example of running a hallucination check using the Python SDK:

```python
from scorable import Scorable

client = Scorable()

request = "Is the number of pensioners working more than 100k in 2023?"
response = "Yes, 150000 pensioners were working in 2024."

# Chunks retreived from a RAG pipeline
retreived_document_1 = """
While the work undertaken by seniors is often irregular and part-time, more than 150,000 pensioners were employed in 2023, the centre's statistics reveal. The centre noted that pensioners have increasingly continued to work for some time now.
"""
retreived_document_2 = """
According to the pension centre's latest data, a total of around 1.3 million people in Finland were receiving old-age pensions, with average monthly payments of 1,948 euros.
"""

# Measures is the answer faithful to my contexts (knowledge-base/documents)
faithfulness_result = client.evaluators.Faithfulness(
    request=request,
    response=response,
    contexts=[retreived_document_1, retreived_document_2],
)

print(faithfulness_result.score)  # 0.0 as the response does not match the retrieved documents
print(faithfulness_result.justification)
```

Another such evaluator is the **Truthfulness** evaluator, which measures the factual consistency of the generated answer against the given context as well as general knowledge.

Here is an example of running the **Truthfulness** evaluator:

```python
result = client.evaluators.Truthfulness(
    request="What was the revenue in Q1/2023",
    response="The revenue in the last quarter was 5.2 M USD",
    contexts=[
        "Financial statement of 2023"
        "2023 revenue and expenses...",
    ],
)
print(result.score)
# 0.5
```

For other RAG evaluators, refer to our [Evaluator Portfolio](https://docs.scorable.ai/quick-start/evaluator-portfolio) page.