Quick answerVerifying that a cited source supports a claim means running an automated check, before the response reaches the customer, that confirms the specific sentence being cited actually contains or entails the claim the agent is making, not just that a plausible-looking document exists and got attached. This is a proactive gate that runs on every response with a citation, distinct from handling a hallucination after a customer has already complained and distinct from how sources are visually displayed in the UI.
Showing a source and having a source that supports the claim are not the same thing
A retrieval step that pulls a real, existing document and an agent that generates a well-formatted citation for it can still produce a claim the source does not actually back. This happens more than teams expect: the retrieved passage is topically related but doesn't state the specific fact claimed, or the agent slightly overstates what the source says while technically citing it correctly. The customer sees a real link and a confident answer and has no easy way to tell the difference between a supported claim and an unsupported one wearing a citation.
Why this needs to happen before the customer sees anything
General hallucination-handling and the post-complaint triage process for deciding whether a bad answer was a hallucination or a product bug both operate after the fact, once a customer has already flagged a problem. This check exists specifically to catch the gap before that point, on every response with a citation, not just the ones that later get complained about. It's a proactive gate, not a downstream investigation tool.
How the check actually works
After the agent drafts a response with a citation, run a second, narrower pass: extract the specific claim being made, extract the specific passage from the cited source, and check whether the passage actually entails the claim, not just whether it's topically adjacent. This can be a smaller, cheaper model call focused purely on entailment, or a rules-based check for simpler claim types (numbers, dates, named entities that should appear verbatim in the source). If the check fails, the agent should either soften the claim to what the source actually supports, or drop the citation and clearly flag the answer as unverified rather than showing a false source.
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This is not the same problem as how sources are displayed
How to design a trustworthy AI agent UI with sources, confidence, and labeling is about how a verified source is presented to build trust, covered separately. This check happens earlier in the pipeline and answers a different question: should this source be shown at all, given what it actually says. A beautifully designed citation UI displaying an unsupported claim is still showing the customer something false; the display layer can only be trustworthy if what feeds it has already been checked.
Where this fits with your broader accuracy work
This check is a natural complement to handling AI agent errors and hallucinations gracefully in a customer-facing product, catching a specific, checkable subclass of the broader hallucination problem before it ever reaches the customer, rather than relying entirely on graceful recovery after the fact. It also changes what you'll see downstream in triaging whether a bad answer was a hallucination or a product bug: fewer unsupported-citation cases reaching that triage queue at all, since this catches the specific pattern earlier.
Track the false-positive rate, not just the catch rate
A verification check that's too aggressive will flag genuinely supported claims as unsupported, frustrating customers with unnecessary hedging or dropped citations. Monitor both how many real unsupported claims the check catches and how often it incorrectly softens a claim that was actually fine, and tune the entailment threshold based on both numbers, not just the catch rate alone.
FAQ
Does this slow down every response with a citation? It adds a small amount of latency for the extra check, but for most teams the accuracy gain is worth a modest latency cost on citation-bearing responses specifically, not every response.
Can this be done with rules alone, without another model call? For narrow claim types (specific numbers, dates, named policy terms) yes. For open-ended factual claims, a lightweight entailment check is more reliable than pattern matching alone.
What happens if the check itself is wrong? Log every case where the check drops or softens a citation, and periodically review a sample by hand, the same discipline used for any automated quality gate, since the check's own error rate needs monitoring too.