Command
Use the Peec MCP to pull the URL report for [specific URL] broken down by date, covering the 30 days before [refresh date] and the 30 days after. For each period show: retrieval rate, citation count, citation rate, and the specific prompts this URL was retrieved for. Then write a clear before and after comparison that highlights: whether retrieval rate improved and by how much, whether citation rate changed, any new prompts the URL started appearing for after the refresh that it was not covering before, and an overall verdict on whether the update moved the needle in AI search.
Content teams refresh articles and then largely guess at whether it helped. Pageview data is noisy. Ranking changes take weeks. There is no direct signal between the work done and the outcome in AI search, which makes it nearly impossible to know what kinds of updates are worth doing again.
This creates that direct feedback loop: retrieval rate before, retrieval rate after, citation rate changes, and a breakdown of which new prompts the refreshed page started covering.
Over time, that data answers the more important question: which types of content updates reliably improve AI search performance?
That is how content strategy compounds instead of just accumulating.
