How should important AI-search content show freshness without rewriting dates just to look new?

Tie freshness signals to substantive changes and make those changes explainable.

For high-change topics, assign an owner and review cadence based on risk. Product specifications, regulations, pricing, platform features, and rapidly changing technical guidance may need frequent review; stable definitions may not.

Store both original publication date and last substantive update date. A substantive update means the answer, evidence, examples, data, recommendations, or referenced standards materially changed. Do not bump dates because a page was re-rendered or a comma changed.

Where useful, add a brief "what changed" note. This helps users and reviewers understand why the page is fresh and makes editorial maintenance auditable.

Check references during each review. A page can have a recent date while citing obsolete studies, dead documentation, or superseded platform behavior. Freshness is about evidence, not decoration.

For machine-readable metadata, keep dateModified aligned with real revisions. Ensure feeds, sitemaps, and structured data do not invent constant updates that contradict the page history.

Prioritize freshness where incorrect information would materially change the answer. You do not need to churn every evergreen page every month.

This approach creates a stronger source: current when necessary, stable when appropriate, and transparent about when information actually changed.