as I sit in front of my computer waiting for a transformation-for-import job to complete, I can describe my basic workflow:
1. define a clean interface target - for me, that's an interface that I made for my startup to import call data.
2. explore the data a little to get a sense of transformation mappings.
3. create a PII-redacted version of the file, upload it to ChatGPT along with the shape of my interface, ask it to write a transformation script in Python
4. run it on a subset of the data locally to verify that it works.
5. run it in production against my customer's account.
I'm curious - that seems like a reasonably standard flow, and it involves a bit of manual work, but it seems like the right tradeoff between toil and automation. Do you struggle with that workflow or think it could be better somehow?
1. define a clean interface target - for me, that's an interface that I made for my startup to import call data.
2. explore the data a little to get a sense of transformation mappings.
3. create a PII-redacted version of the file, upload it to ChatGPT along with the shape of my interface, ask it to write a transformation script in Python
4. run it on a subset of the data locally to verify that it works.
5. run it in production against my customer's account.
I'm curious - that seems like a reasonably standard flow, and it involves a bit of manual work, but it seems like the right tradeoff between toil and automation. Do you struggle with that workflow or think it could be better somehow?