Comparison
Faithfulness vs Hallucination
Faithfulness and Hallucination 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 Hallucination
Hallucination comes up when the question is fundamentally about evaluation.
Citing a paper that does not exist.
Frequently asked
What is the difference between Faithfulness and Hallucination?
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. Hallucination: A hallucination is a confidently-stated, plausible-sounding LLM output that is factually wrong. It is the failure mode that most often surprises non-expert users.
When should I use Faithfulness vs Hallucination?
Always for RAG — faithfulness is the single most actionable production metric. Hallucination applies when you are focused on evaluation.
Are Faithfulness and Hallucination the same thing?
No. Faithfulness is evaluation; Hallucination is evaluation. They are related but address different parts of the AI stack.