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Anchor Phrases Beat Verbatim Quotes for Reliable AI Fact Extraction

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A developer tested two methods for extracting facts with source provenance from AI models: asking for verbatim quotes versus retrieving short anchor phrases of three to eight words. Verbatim quote accuracy varied widely across model tiers, ranging from just 37% to 93%, with some models silently fabricating plausible-sounding but incorrect quotes. The anchor phrase approach, which locates a real substring in the source document, achieved coverage of 93% to 100% across all tested model tiers. The anchor method also fails explicitly when no match is found, rather than returning a hallucinated citation. The findings highlight that extraction accuracy and extraction reliability are distinct problems, and that a model which quietly quotes incorrectly is more dangerous than one that admits uncertainty.

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