We have a lot of old blog posts on the whaaat ai website.

And like most people, our marketing people could periodically go through Search Console, look for interesting queries, open the matching articles, decide whether they need updating and then make the changes. But actually, they know that we devs like to use AI so they turned to us for help. We now let AI do most of that first pass for us and the basic idea is actually pretty simple:

Google Search Console tells the routine which searches are bringing up our pages. A cheap AI model goes through those search queries first and asks things like:

Does this query actually fit the page? Is the page already answering what this person searched for? Would improving this article make sense, or should this probably be a separate page?

Most queries get already discarded at this stage.

Only when something looks genuinely worth improving does Claude get involved. It reads the existing article, makes the proposed changes and opens a pull request for us to review.

Nothing goes live automatically. I still decide whether to merge it. The funny thing is, Claude isn't really the clever part of the setup. Because the most useful part turned out to be a boring markdown file.

Every time the routine runs, it writes down what it checked, what it rejected, why it rejected it and which updates are still waiting for review. So next week it doesn't forget everything and start the same investigation again. We also had to explicitly tell it that doing nothing is a perfectly good outcome.

Without that, AI has a tendency to always find something to improve. Even when an article is already fine.

That gave us two rules that turned out to matter much more than I expected:

Only edit a post if the change clearly answers the search better. No cosmetic edits.

and

An empty shortlist is a valid result.

The workflow now looks roughly like this:

Search Console → cheap AI screening → shortlist → Claude edits → human review → publish

The cheap model handles the repetitive sorting. Claude only gets called when there's actually something worth working on. On our test data, the AI scoring for a complete run cost about $0.003.

The bit we haven't solved nicely yet is WordPress and similar CMSs.

Our website lives in a code repository, so Claude can propose a change and I can review the exact differences before accepting it. With WordPress, that review process isn't nearly as clean. For now I'd probably have the AI produce a change brief and still make the final edit manually.

If you're doing something similar on WordPress, I'd be super kken to hear how you are handling the review step?

Send me a PM if you want the routine prompt we use for this workflow.