For people who don't read Chinese: the two 'yi' characters numpad0 mentioned (义 and 義) are the same, but written in different variants of Chinese script (Simplified/Traditional).
Our core product is an analytics product that lives on people's website, so we're able to tell what are the main pages that people are reaching with LLMs
For our in-app AI visibility product we use that information for finding prompts at topics that are being used to reach those pages
For this public tool instead we do a best guess of what are reasonable queries companies would want to show up for and run them against Google and ChatGPT
we do a best guess of what are reasonable queries companies would want to show up for
Got it.
I've done this before using an LLM, but I mistakenly thought you had some magic source (magic sauce!) of actual LLM queries. Sometimes it's not the obvious stuff.
If you want to do one email for expired users and another for non expired users and another email for users that somehow have a date problem in their data....
Well, in that case you wouldn't want to pipe them all through generateExpiryEmail.
But perhaps you can write a more generic function like generateExpiryEmailOrWhatever that understands the user object and contains the logic for what type of email to draft. It might need to output some flag if, for a particular user, there is no need to send an email. Then you could add a filter before the final (send) step.
