Quote:
Originally Posted by id10t
I do a lot of ETL work based on CSV or CSV-like files. I can copy/paste a header row into chatgpt and have it generate a Java POJO that will import the file and match based on hte OpenCSV bindings/annotations, and then ask it to create a DB2 create table sql statement.
I used to do the same thing via shell scripting and complex search/replace stuff but even having to "fix" a few things on the SQL statement (variable types and sizes, add some columns to match our internal standards, grant statements) it is much quicker/easier to do it via chatgpt than command line.
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Something like that it’s hopefully apparent immediately if it messes up. Or maybe you don’t care if it’s perfect. I’ve had random columns of data deleted when I asked an array of structures to be reorganized, is very frustrating to me when the tool isn’t reliable. I’m honestly not sure when I’ll be able to trust it.
I’m writing compiler, libraries and system code in c++ and there’s a lot that needs to be done a certain way that can’t be caught through testing. We critically cross examine each other in code review and everything needs to be clear and justified. If I can’t get clear reasons and explanations and design choices for a blob of code then it’s not going in.
I have used it to try and write tests to exercise boundary conditions and it is successful about 25% of the time which is great for how quick it is, but even then everything it touches requires careful verification.