What's the difference between faithfulness, groundedness, and context presence?

They check three different points in the same pipeline. Context presence comes first and asks whether the information needed to answer was retrieved at all; if it wasn’t, the failure is in retrieval, before the model has written a word. Groundedness comes next and asks whether the answer actually came from that retrieved context or from the model’s own prior with the context sitting unused, which matters because an ungrounded answer can still be correct whenever the prior happens to be right. Faithfulness comes last and asks whether the answer accurately represents the source it cites, catching the case where retrieval and grounding both worked and the model still misquoted or inverted what it read.

Knowing which one fired tells you where to look. A faithfulness failure means fix the generation step; a context-presence failure means fix retrieval.

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How can an answer be faithful to its context and still be wrong?Faithfulness only checks the answer against what was retrieved, so an answer built entirely from a partial or unrepresentative slice of the source passes even though it's wrong.answer →How do I catch agent hallucinations when my error rate stays flat?Error rate tracks whether a run crashed, not whether its answer was true, so you need a check that compares the answer's content against its actual sources instead.answer →What is a hallucination in an AI agent?An agent stating something its sources don't support, whether it misquotes a document it retrieved, ignores that document entirely, or never retrieved anything to begin with.answer →Everything on hallucinationsThe definition, and every question answered under hallucinations.concept →

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