“Would a good comma separated tabular databases from customer data regarding a beneficial relationships software into the following the articles: first-name, history term, years, urban area, condition, gender, sexual orientation, appeal, level of wants, level of fits, day consumer joined this new application, together with user’s get of app between step 1 and you will 5”
GPT-step three did not provide us with people line headers and you can provided united states a table with every-most other line with no information and just 4 rows regarding real customers studies. It also provided united states about three articles regarding hobbies when we was in fact simply trying to find you to, but getting fair to GPT-step 3, i performed have fun with a good plural. All that becoming told you, the content it performed develop for all of us actually half crappy – brands and sexual orientations tune with the proper genders, this new metropolises it provided united states are also within best claims, together with schedules slip within this the ideal variety.
Hopefully if we provide GPT-step 3 some examples it does ideal see just what we have been appearing getting. Regrettably, because of unit limits, GPT-3 are unable to see an entire database to understand and you may build synthetic analysis from, therefore we can only provide several analogy rows.
“Would a beneficial comma separated tabular database that have column headers from fifty rows out of consumer study from an internet dating application. 0, 87hbd7h, Douglas, Trees, thirty-five, Chi town, IL, Male, Gay, (Cooking Decorate Discovering), 3200, 150, , step three.5, asnf84n, Randy, Ownes, twenty two, Chi town, IL, Men, Straight, (Running Hiking Knitting), five hundred, 205, , step three.2”
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