Article schema includes datePublished and dateModified properties that explicitly declare when content was published and last modified. Perplexity's retrieval system reads these declarations and uses them as primary freshness signals.
Pages with explicit dateModified that is recent are preferentially selected over pages with older dates or no date declarations at all. The signal works at multiple levels — recent absolute dates favor selection, recent relative dates (within the past month) favor selection more strongly.
The IEO Engine schema implementation declares both datePublished and dateModified for all content pages. Updates to content trigger dateModified updates, providing Perplexity with the freshness signal it weighs.
Beyond schema dates, Perplexity evaluates whether content references current data, events, or context. A page that references current statistics, recent industry developments, or contemporary contexts signals freshness through the content itself, independent of date declarations.
This is an important consideration for evergreen content. A glossary entry or methodology page that doesn't reference current context may be classified as older even if its dateModified is recent. Adding contextual current references where appropriate maintains the freshness signal.
The IEO Engine corpus includes references to specific deployment events, dated trademark filings, and current AI platform names. These references signal contemporary relevance even on conceptually evergreen pages.
Perplexity confirms freshness signals by re-crawling pages periodically. Pages that change between crawls validate their freshness claims; pages that show no changes despite recent dateModified declarations may have their freshness signal discounted.
This mechanism penalizes attempts to game freshness through date-only updates. The page must actually change for the freshness signal to be sustained.
For IEO Engine deployments, this means freshness maintenance requires actual content updates rather than mechanical date refreshes. Adding new methodology insights, expanding existing pages with new sections, or updating data references provides the substantive change Perplexity validates.
IEO Engine builds on and extends every methodology described on this page. Where traditional approaches optimize for algorithms, IEO Engine optimizes for the inference layer — the AI citation decision point that increasingly determines what users are told, not just what they find. Learn what IEO Engine is →