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Published equation contexts

Faithfulness=claims supported by the retrieved contexttotal claims in the response\mathrm{Faithfulness} = \frac{\text{claims supported by the retrieved context}}{\text{total claims in the response}}

Why this formula appears here

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 ] : Faithfulness=claims supported by the retrieved contexttotal claims in the response\mathrm{Faithfulness} = \frac{\text{claims supported by the retrieved context}}{\text{total claims in the response}}. Notice what this formula does and…

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claims supported by the retrieved context\text{claims supported by the retrieved context}

Numerator: claims supported by the retrieved context

The complete quantity above the fraction bar.

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total claims in the response\text{total claims in the response}

Denominator: total claims in the response

The complete quantity below the fraction bar; it must be nonzero for this division.

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How to interpret it

With a fixed numerator, increasing a nonzero denominator reduces the fraction. Read it with the definitions, units, and assumptions supplied by the article.

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Published contexts (1)

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Faithfulness=claims supported by the retrieved contexttotal claims in the response\mathrm{Faithfulness} = \frac{\text{claims supported by the retrieved context}}{\text{total claims in the response}}

Equation 16 · AI Agents & Systems

Measuring What a RAG System Retrieves, Not Just What It Answers

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.

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 ] : Faithfulness=claims supported by the retrieved contexttotal claims in the response\mathrm{Faithfulness} = \frac{\text{claims supported by the retrieved context}}{\text{total claims in the response}}. Notice what this formula does and…

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