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A HireQwik project write-up says its earlier AI quote-checking rule often marked interview quotes “unverified” because it searched only a candidate’s roughly 10 most recent transcript turns. When the author checked the full transcripts, most of the flagged quotes were present. The fix was to search the full transcript and distinguish evidence that is absent from records that cannot be checked.
What “wrong” meant in this project
HireQwik’s 2026 account concerns a quote-grounding check in one hiring workflow—not the accuracy of AI hiring decisions generally. The earlier checker’s output was “unverified”; that did not establish that a quote was fabricated or absent. The author described the earlier result as 15%, while the article title says “1 in 6.” The account does not define a denominator that makes those figures directly reconcilable, so neither should be treated as a precisely established general error rate.
The author reports that the earlier verifier marked 162 of 166 knockout quotes as unverified. On review of the full transcripts, the author says 163 of the 166 quotes appeared word-for-word or close to it, one was genuinely missing, and two records could not be checked because the stored text was a bracketed description such as “no audible response.” These are the author’s reported counts, not independently audited results. In particular, the 162 flags are not 162 confirmed false quotes.
Why a recent-message window missed evidence
The old check compared a quote’s beginning with only the candidate’s roughly 10 most recent turns. That window could exclude the relevant words if the candidate had said them earlier and continued speaking. The author points to speech-to-text segmentation as a factor: a long spoken answer may be stored as many short transcript turns rather than one continuous response.
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In this project’s data, the author says about 37% of candidate turns contained four words or fewer. That project-specific figure helps explain why a window measured in turns could cover less speech than expected; it is not a general statistic about interviews. A short-turn-heavy transcript can push relevant evidence beyond a fixed count of recent messages even when the quote is present in the interview.
How the revised check works
The company write-up describes a single shared grounding check, in use since 8 August 2026. It applies the same process to the quote and transcript, then reports one of three states:
- Normalize the text: lowercase it, strip punctuation, and collapse spaces.
- Check short quotes exactly: for quotes of six words or fewer, require an exact normalized match somewhere in the candidate’s transcript.
- Check longer quotes by overlap: split the quote into runs of six consecutive words, then require at least one third of those runs to appear in the transcript.
- Search the full transcript: do not limit the comparison to recent turns.
- Return a meaningful status: label the quote “grounded,” “not grounded,” or “unknown.”
For example, the author says a 16-word quote yields 11 overlapping six-word runs; three matching runs meet the stated one-third threshold. That is a reported implementation choice, not a generally validated standard. Normalization also does not make every transcription variation equivalent: “cannot” and “can’t” remain different, though other matching runs may still provide enough overlap in the author’s example.
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Why “unknown” matters
A verifier should not turn missing or unusable data into a verdict about the quote. If the stored record is a bracketed note such as “no audible response,” there may be no transcript text against which to check a quotation. “Unknown” preserves that distinction; “not grounded” should mean that the check found no supporting match under its rules, not simply that the evidence was unavailable.
Making the result traceable to the underlying transcript is equally important. A reviewer should be able to inspect the text that produced a grounding decision, rather than treating a checker’s label as proof that a quote was or was not spoken.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the correction does—and does not—show
HireQwik says a background task stamps recent interviews about once a minute, and a one-off task processed historical knockout quotes. Those operational details describe how the revised check was applied; they do not establish its accuracy on new interviews.
The write-up provides no independent benchmark or revised-check error rate on a fresh or external dataset. Its short-quote exact-match rule and long-quote overlap threshold are specific design choices, not proven best practices for other systems. The project author’s stated lesson was: “The lesson I took was not ‘our AI is fine.’” The more limited takeaway is that a checker can be wrong about where it looks for evidence, and its output needs to preserve uncertainty as well as expose the evidence behind it.
HireQwik’s project write-up, “AI Hiring Hallucination: Checking Quotes Before a Reject” is the source for the company’s account and figures.
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