A QA team is preparing to release a Foundry-hosted assistant that answers questions using a retrieved knowledge base of internal manuals. Before release, they want an automated metric that scores whether each generated answer is actually supported by the documents it retrieved, so they can flag responses where the assistant states something the source material never said. Which evaluation metric fits this need?
Select an answer to reveal the explanation.
Short Explanation
Before letting a knowledge-base assistant loose on real users, you want a way to catch it making things up, saying something confident-sounding that the source manuals never actually said. That's a different problem from how fast it responds, how many tokens it burns through, or whether it uses offensive language, so none of those measurements will catch a fabricated claim. What you need is a check that specifically compares each generated answer against the documents it pulled up and asks whether the answer's claims are actually traceable back to that source text. If the assistant states something the manuals don't support, that check flags it as unsupported, even if the wording sounds perfectly reasonable and the response came back quickly and safely. That's the gap this QA team is trying to close before release, and it calls for a metric built specifically to compare stated answers against retrieved evidence.
Full Explanation
The correct answer is A. Groundedness is the evaluation metric designed to check whether a generated answer is actually backed up by the retrieved source documents, which directly catches cases where the assistant states something the manuals never said, the exact failure mode this QA team wants to detect before release. Option B is incorrect because latency measures response speed in milliseconds, which says nothing about whether the content of the answer is factually supported by retrieved material. Option C is incorrect because a token-based cost metric tracks how much a response costs to generate, which is a budgeting concern unrelated to whether the stated facts trace back to the source documents. Option D is incorrect because content filter severity metrics log how often categories like violence or hate speech are triggered, which addresses harmful content rather than unsupported or fabricated factual claims. Since the team's concern is specifically that the assistant might assert things beyond what its retrieved documents actually contain, the metric built to compare generated claims against source text, groundedness, is the one that answers their question.