Adam if you're starting from Clay's LinkedIn Enrichment, I would start with a column that pulls the experience objects from the LinkedIn enrichment where the end_date property is null (all of the companies that list a "Present" end date) using this formula:
JSON.stringify({{Enrich Person from LinkedIn Profile}}?.experience?.filter(item => !item.end_date))
Then I'd feed the value of that column into GPT 3.5 with the following prompt.
Review the following JSON data representing a LinkedIn user's work experience, and return ONLY a comma-separated list of the values of each `url` property where the `title` property equals a job title that is some variant of "CTO" or "VP of Engineering". DO NOT INCLUDE ANY OTHER COMMENTARY OR FORMATTING SUCH AS SINGLE OR DOUBLE QUOTATION MARKS - ONLY THE COMMA SEPARATED LIST. If you are unable to make a determination, or if there are no matching criteria, RETURN ONLY AN EMPTY STRING. Here is the JSON to review:
```
{{Present Companies}}
```
from there you can prompt Claygent with:
1.) Visit each of the following list of LinkedIn company profile URLs
2.) For each company page, navigate to the "About" tab, and determine whether or not the "Company Size" section contains the value "1,001-5,000 employees".
3.) If ANY of the companies on the list satisfies that criteria, return `true`. Otherwise, return `false`. If you are unable to make a determination, also return `false`.
Here is the list of LinkedIn company URLs:
```
{{CTO/Engineering URLs Response}}
```
Any rows with a true value from that last column are matches.
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