“Manage good comma split up tabular database out-of consumer research of a relationship software towards the adopting the columns: first name, history name, decades, town, state, gender, sexual orientation, welfare, level of wants, amount of suits, day customer joined the software, therefore the user’s get of one’s app ranging from step 1 and you will 5”
GPT-step 3 didn’t give us people column headers and you can offered all of us a table with every-other line having zero advice and just 4 rows from actual consumer research. In addition provided us three columns out of appeal as soon as we had been only looking for you to, however, to be fair so you’re able to GPT-3, i performed fool around with a great plural. All of that getting said, the data it did establish for all of us is not 1 / 2 of crappy – labels and you will sexual orientations tune towards correct genders, the brand new places they provided all of us also are within their correct states, in addition to times slide within a suitable assortment.
We hope when we promote GPT-step three some examples it does ideal understand what the audience is searching having. Unfortunately, because of device limits, GPT-step three are unable to see an entire databases understand and you may generate artificial data of, therefore we are only able to provide a few analogy rows.
“Do a great comma broke up tabular database which have column headers away from fifty rows off customer data regarding a dating application. 0, 87hbd7h, Douglas, Woods, 35, Chi town, IL, Male, Gay, (Baking Painting Training), 3200, 150, , 3.5, asnf84n, Randy, Ownes, twenty two, Chicago, IL, Male, Upright, (Powering Walking Knitting), 500, 205, , 3.2”