The Faithfulness metric is one of several non-deterministic Metric Types Galtea uses to evaluate the factual alignment between the model’s generated response (actual_output) and the information found in the retrieval_context. It is a core indicator of hallucination risk in retrieval-augmented generation systems. A high faithfulness score indicates that the model grounds its answer in retrieved content, rather than introducing unsupported or fabricated information.

​
Evaluation Parameters

To compute the faithfulness metric, the following inputs are required:
  • input: The user’s original prompt.
  • actual_output: The LLM-generated response.
  • retrieval_context: The retrieved passages or nodes used by the model.

​
How Is It Calculated?

The score is computed using the following steps:
  1. Fact Comparison: An LLM analyzes whether the statements made in actual_output are substantiated by the retrieval_context.
  2. Hallucination Check: The LLM flags any unsupported claims or discrepancies.
The final metric is calculated as: Faithfulness=Number of factually aligned outputsTotal number of evaluated outputs\text{Faithfulness} = \frac{\text{Number of factually aligned outputs}}{\text{Total number of evaluated outputs}} This helps teams monitor the risk of hallucinations and improve trust in generated responses.
This metric was incorporated to the Galtea platform from the open source library deepeval, for more information you can also visit their documentation.

​
Suggested Test Case Types

The Faithfulness metric is effective for evaluating all types of quality test cases in Galtea, for products that use RAG, since it measures the model’s ability to maintain alignment with retrieved context: