Translation where context is whole apps, spread over dozens or hundreds of files, with complex interdependencies is also a form of integration. And chatGPT doesn't have the context size to do it, maybe the next GPT. But 4000 tokens is not enough, 50-100K tokens would be more like it.
Well.. yes, it is a stretch to use it for a large code base, but especially if they are all relatively small/medium sized files, a directory listing can get you pretty far to select the relevant files. You can also do a vector (embedding) search to find relevant functions/files and feed that into the prompt.
Also the OpenAI coding model code-davinci-002 has an 8000 token max not 4000 like text-davinci-003.
You can ask chat GPT now the following question "Given this Json as input, and this json as output, write code that transforms the input to output", and it will get it right. Try it out sometime, and then realize that like 80% of backend processing.
There just needs to be more targeted training, and some system built around it to write a complete service code, and you can replace a good number of jobs solely through that.
20 yoe. Never had to write code to transform json into json in a pure way. Maybe XML to XML … once. Good ol XSLT!
By pure I mean with no other requirements such as accessing another data store and running a bunch of rules decided by having several meetings with various people to find out what is actually required… a bit like what chatGPT doesn’t do.
Ya, it's tricky to map that comment to a useful scenario, but maybe getting a json document from an external service and saying "write a function that parses this json and returns this TS structure, but I think that would be a rare efficiency killer for me
Generally transforming the data is not the hard bit, it's specifying the shape. You're not replacing any meaningful jobs by getting GPT to automatically translate one schema to another, you're improving the productivity of existing devs.
