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Recency bias is the tendency of LLMs, particularly those using retrieval-augmented generation, to favor recently published or recently updated content over older pages on the same topic. A page updated this month is more likely to be retrieved and cited than an equivalent page last updated two years ago.

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Why it matters

Recency bias creates both an opportunity and a risk. The opportunity: brands that publish or refresh content regularly gain a systematic advantage over competitors with stale pages. The risk: brands that stop updating lose AI visibility gradually as their content ages out of retrieval preference. Perplexity shows the strongest recency bias of the tracked platforms; Claude shows the least because it relies primarily on training data.

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How it applies in practice

Cited tracks content freshness as a diagnostic factor in mention rate analysis. When a brand’s visibility drops without a clear content or competitive cause, stale content is one of the first hypotheses. The recommended response varies by category: fast-changing verticals (tech, fintech) benefit from monthly content refreshes, while stable categories may sustain visibility with quarterly updates.