My start-up, Transpose Health, has a core data conversion engine, but data work also has a lot of random stuff that needs to be done. AI coding has been transformative for this. What might've taken a few hours to code (and test) now takes a couple minutes to prompt ChatGPT for, then a minute for it to code.
My prompt:
write me a python script that:
- opens up <excel file> (an excel file)
- loops through all CSVs in <folder> that are CSVs and do not contain the word "Copy" (table 2)
- inner joins table 1 to table 2 on "Legacy Rx" and "PrescriptionNumber" (to only get the rows from table 2 that are in table 1)
- prints out all of the messages from the "hl7_str" column in table 2 into a txt file (separate the "hl7_str" rows with a return character)
Of course, ChatGPT was not perfect the first time around. I had to (a) prompt ChatGPT it to be less verbose (I needed a quick-and-dirty script I could review quickly, not an industrial-grade one), (b) run the script, (c) debug it (it had a small bug, so I had to do a deeper code review), (d) then test the outputs and iterate. Overall, the end-to-end process took ~30 minutes, but I saved significant time and brain energy on the coding part.
(Interesting that my value-add shifts from understanding/writing the code to understanding how to prompt the LLM and QA the outputs. In other words: AI is taking all the technical implementation work from me, for better and for worse!)
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