Faithfulness is the eval metric that measures whether every claim in a model's response is derivable from the retrieved context. It's the canonical anti-hallucination check for any RAG workflow. A faithful answer might omit relevant facts, but it never invents new ones.
How it's measured.
Faithfulness is almost always LLM-graded. A grader model receives the retrieved context, the model's response, and a rubric: for every claim in the response, does the context support it? Score is the fraction of claims that pass.
Faithfulness vs. accuracy vs. coverage.
- Faithfulness — Are all claims in the answer supported by the context?
- Accuracy — Is the answer correct in the real world (regardless of context)?
- Coverage — Does the answer include all the relevant facts the context contained?
A model can be perfectly faithful (everything traces to context) but wrong (the context was wrong). Faithfulness measures the model's discipline, not the system's correctness.
Production targets.
Faithfulness ≥ 0.95 is the bar for any RAG workflow that reaches end users. Below that, the system regularly invents facts and the brand cost is high. Faithfulness is a tight- tolerance regression gate metric: drop > 0.02 vs. baseline fails the release.
Frequently asked.
- What is faithfulness in AI evaluation?
- Faithfulness is the eval metric that measures whether every claim in a model's response is derivable from the retrieved context. It's the canonical anti-hallucination check for RAG workflows. A faithful answer might omit relevant facts, but it never invents new ones.
- Is faithfulness the same as accuracy?
- No. Faithfulness asks 'does the answer trace to the context?' Accuracy asks 'is the answer correct in the real world?' A model can be perfectly faithful but wrong (because the context was wrong), or accidentally accurate but unfaithful (because it guessed correctly despite no context support). Production needs both, but they're measured separately.
- How do I measure faithfulness?
- LLM-graded: a small grader model receives the context, the response, and a rubric. For every claim in the response, it scores whether the context supports it. The fraction of supported claims is the faithfulness score. Production targets are typically 0.95+, with a tight regression-gate tolerance (≤ 0.02 drop).
- What if the model gives a faithful but useless answer?
- Then your coverage metric is too loose. Faithfulness and coverage are complementary — measure both. A faithful but useless answer scores high on faithfulness and low on coverage. The right release gates score both and fail if either drops.