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How to detect AI hallucinations before users do

Detect hallucinations by comparing outputs with trusted evidence, checking claims individually, testing missing-information cases, and monitoring correction patterns in real use.

For product teams responsible for AI answers that users may trust or act on. · Reviewed 2026-09-29

Classify the failure

  • Unsupported fact: the source does not contain the claim.
  • Wrong interpretation: the source exists but the answer misreads it.
  • Invented citation: the link or document does not support the sentence.
  • Fabricated number: a statistic, price, date, or calculation is wrong.
  • Overconfident uncertainty: the system should have refused or asked for clarification.

Build a detection loop

Create a reviewed test set, run the system against it, compare each material claim with evidence, and record the failure type. In production, sample outputs by risk and feed confirmed failures back into the evaluation set.

Signals worth monitoring

SignalWhat it may reveal
High correction rateThe system produces fluent but unusable output
Citation clicks that do not support claimsGrounding or source selection failure
Frequent retriesThe task or prompt is unstable
Answers with no source when one is requiredMissing evidence or weak refusal behavior
Different answers to the same inputUncontrolled randomness or context drift

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