What does this equation mean?

Ragas is a widely used reference-free evaluation framework built explicitly around this separation: it scores retrieval effectiveness, the faithfulness with which a model uses retrieved passages, and generation quality, without requiring a human-written ground-truth answer for every query [ 4 ] . Its faithfulness metric is defined mechanically rather than as a vague notion of “sounding grounded”: a response is decomposed into individual factual claims, each claim is checked against the retrieved context to see whether it can be inferred from it, and the score is the resulting ratio, as the framework’s own documentation states [ 5 ] : $\mathrm{Faithfulness} = \frac{\text{claims supported by the retrieved context}}{\text{total claims in the response}}$. Notice what this formula does and does not depend on. It does not depend on whether the retrieved context was the right context — a response can be perfectly faithful to a passage that answers a different question than the one asked, and the faithfulness score will not fall. It depends only on the relationship between the written output and whatever was placed in front of the model. Answer relevance and groundedness metrics, which the same documentation lists alongside faithfulness, complete the picture from the other side: relevance checks whether the response actually addresses the question rather than merely being consistent with the context, and groundedness-style checks look for unsupported additions the model introduced beyond what any retrieved passage stated [ 5 ] .