Professionals generally fall into two categories: 1) people who are handy with Excel, Google Docs, merging, VLOOKUPs, etc., like David Galley, and 2) those who try to avoid working with tables, like me. I am glad to have David on the team, but I can’t bug him with every table-related task.
ChatGPT Code Interpreter (you must have a $20/mo account and select “Code Interpreter” from Settings) is a big helper for mundane tasks that are part of any sourcing process. I will go over a use case – part of my sourcing process for most projects – where Code Interpreter automates work with tables.
“Code Interpreter” is like an AI Data Analyst. You can upload files (one at a time); it will process them, give you a file to download, and report on the process it went through. It can produce clever summaries and graphs. The simple data conversions required in sourcing are a breeze for the system.
If I source, populate a project on LinkedIn Recruiter, and export files (25 records at a time,) Code Interpreter can merge the files, clean up encoding, and clean up the names, leaving first and last, e.g., Mr. Joe A. B. Doe, Esq –> Joe, Doe.
Intending to contact the potential candidates, I run the (merged) list through contact-finding systems: SalesQL, ContactOut, and SeekOut. All of them have bulk uploads.
Code Interpreter can merge the contact finders’ resulting tables with the original Recruiter export. I love that no coding or formulas are required to interact with the tool. Here is my “VLOOKUP” expressed in English. The file referenced at the top of the screenshot is a cleaned-up Recruiter export; I am uploading a SalesQL export for the same profiles.
I repeat the process with exports from the two other contact-finding systems by telling the system, “Do the same,” and uploading the relevant exports.
As the last step, I ask the tool to put all emails in one column and deduplicate.
When all is finished, the resulting table contains the original data from Recruiter; the email column is populated with data from the four systems. Since these are already vetted profiles, the output can go to the client for sourcing projects or serve as a contact list if it is full-time recruiting.
(I admit that for me, in a sense, this application of the tool is “too late” since I already have consultant-written custom scripts that do the above. But I took it as a practical, familiar example of using Code Interpreter. The process was fast.)
I also asked Code Interpreter for a summary of the file and got a solid report:
I was working with a small record set (about 40). For a larger group of profiles, a summary like this helps to understand the market and communicate with the Hiring Manager.
What impressed me throughout the dialog with Code Interpreter was that I felt a good level of understanding (if you know what I mean) compared to other AI “companions.” The tool understood things I said very briefly. It kept the content and explained what it was doing, and I never had to correct it in the experiment.
As a generalization, look into Code Interpreter if you do not like writing formulas (like some of us) but have to deal with data (as we all do). 🙂
Please check out my upcoming class –
on August 2, 2023. We will go over many tools and applications “in plain English.”