Recency Weighting

Extended definition

Recency Weighting describes the algorithmic preference AI engines apply to recently published content versus older, potentially more authoritative sources. High recency weighting means fresh content gets prioritized even if less comprehensive; low weighting means authoritative older content maintains dominance. Different engines use different recency curves: some strongly favor content from past 30 days, others weight past year equally, some ignore publish dates entirely. Recency weighting varies by query type: news queries weight heavily toward recent, evergreen topics weight toward authority regardless of age.

Why this matters for AI search visibility

Recency Weighting determines whether your content has a shelf life or compounds value over time. High recency weighting means you need continuous publishing to maintain visibility; older content loses citation potential. Low weighting means high-quality content maintains value for years, but breaking into citations requires displacing entrenched sources. For content strategy, understanding recency curves helps balance production velocity (feed high-recency engines) versus quality investment (win low-recency engines). Misunderstanding weighting causes strategy failures: treating all engines like high-recency search means wasted effort on low-recency platforms.

Practical examples

• Perplexity strongly weights content from past 60 days; 85% of citations are to recent sources versus only 34% for ChatGPT
• Evergreen guide from 2019 maintains citations on low-recency engines but dropped to zero on high-recency platforms
• Weekly publishing cadence increases visibility 4.2x on high-recency engines but only 1.3x on low-recency platforms