[bluefirebrand]

If you don't understand the format yourself how can you ever trust that GPT is actually giving you an accurate result?

The problem with using an ML model to parse stuff you don't understand is that you then have no way to verify the accuracy

If there are no stakes to the results then that's fine to trust blindly but if this is something you need for your job, that's risky and franky stupid to trust to ML

[CHY872]

Yes, if you take a problem you don't understand, ask a GPT to write a solution, do nothing to check the solution, trust it blindly, and then use the solution for some safety critical problem, you're playing with fire. But there's a spectrum, and please don't assume I'm at that end of it.

Lots of the time you understand the problem, but the problem is repetitive. Parsing a weird file format might well be that. Beyond that, you have solutions that are easily checked. For example, if I ask ChatGPT to optimise an algorithm for a certain CPU cache, I can easily read whether it did that. And then, there are parts of a software job that are crucial and subtle, and parts that are not.

As a practitioner, traditionally that leads to a shift in the focus and speed with which you approach a task - some pieces of code are 100 lines that took you 2 weeks to get to and were hard fought, some are 2000 lines which you wrote in a day.

Lastly, so much of solid software is being able to understand a probably unfamiliar domain, and ChatGPT can be a great buddy in terms of gaining problem context, finding the limits of your own understanding.

I don't use co-pilot like things, but I've found ChatGPT to be a massive enabler in terms of being able to be productive in unfamiliar problem-spaces.