Reviewing Answer Accuracy
The dedicated Hallucination Detection service is retired. Review saved prompt answers and available citations against approved brand facts instead. Monitoring does not automatically verify facts or guarantee that a source update will change future answers.
Why this matters
Large language models generate answers by pattern-matching across training data and retrieved sources, not by looking up a canonical fact sheet for your brand. That means a model can confidently state a price you no longer charge, a feature you never shipped, or a founding date that's simply wrong — and repeat it consistently across sessions until the underlying sources it drew from are corrected. Left unaddressed, these claims shape how prospects and customers understand your brand before they ever reach your site.
Claims worth reviewing
- Outdated pricing or plan details: a model citing a discontinued tier, an old price point, or a feature that has since moved between plans.
- Feature claims: capabilities attributed to your product that don't exist, or real capabilities described inaccurately enough to set the wrong expectation.
- Company facts: founding year, headquarters, funding status, or leadership details that drift from what's actually true.
- Competitive mischaracterization: claims that misstate how you compare to a named competitor, in either direction.
What you can interact with
- Provider selection: review potential issues per AI platform.
- Brand scope: narrow findings to a specific brand/product line.
- Refresh: re-check after you publish corrections or clarifications.
What the data represents
- Potential inaccuracies: statements that may be wrong, outdated, or overly confident.
- Uncertain areas: topics where AI answers vary or lack clear support.
- Confidence signals: guidance for triage, not a guarantee of truth/falsehood.
How we get this data
Scheduled prompt runs capture observed answers and available citations. Compare the original answer and cited source with your current documentation before concluding that a statement is incorrect. Coverage and refresh cadence depend on the provider and your plan, not every AI conversation.
Recommended response workflow
- Confirm the claim internally (what is correct today?).
- Update your official pages to state the correct information clearly.
- Add an FAQ entry for common misconceptions.
- Re-check later to confirm the issue is decreasing over time.
Where this fits in your workflow
Use prompt history and citation evidence to review whether subsequent answers reflect your source improvements. Record the changes you make and avoid treating a single run as proof of a lasting result.
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