Comparison
Faithfulness vs Online Evaluation
Faithfulness and Online Evaluation are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Faithfulness
Always for RAG — faithfulness is the single most actionable production metric.
Faithfulness eval flags an answer that cited "California enacted X in 2024" when the retrieved policy said 2023; the trace surfaces the original failure.
When you would reach for Online Evaluation
After offline eval is solid and you have meaningful production volume. Stretch your eval coverage from a fixed set to a live one.
Phoenix running a faithfulness eval on 5% of production RAG traces, dashboard charts the rolling 7-day mean.
Frequently asked
What is the difference between Faithfulness and Online Evaluation?
Faithfulness: Faithfulness measures whether an LLM's answer is supported by the retrieved context — every claim either appears in the source material or follows directly from it. The most important RAG quality metric. Online Evaluation: Online evaluation runs scoring functions over live production traffic — usually a sample of recent traces — to monitor quality continuously instead of relying solely on a fixed offline dataset.
When should I use Faithfulness vs Online Evaluation?
Always for RAG — faithfulness is the single most actionable production metric. After offline eval is solid and you have meaningful production volume. Stretch your eval coverage from a fixed set to a live one.
Are Faithfulness and Online Evaluation the same thing?
No. Faithfulness is evaluation; Online Evaluation is evaluation. They are related but address different parts of the AI stack.