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Yeah, a lot of times it has better code quality, but more subtle bugs than what I'd be prone to produce.

I think a lot of the criticisms are premature, and it's more a stumbling step forward with need for support from additional infrastructure.

Where's the linter integration so it doesn't spit out a result that won't compile? Where's the automatic bug check and fix for low hanging fruit errors?

What should testing look like or change around in a gen AI development environment?

In general, is there something like TDD or BDD that is going to be a better procedural approach to maximizing the gains to be had while minimizing the costs?

A lot of the past year or two has been dropping a significant jump and change in tech into existing workflows.

Like any tool, there's the capabilities of the tool itself and the experience of the one wielding it that come together to make the outcome.

The industry needs a lot more experience and wisdom around incorporation of gen AI in development before we'll realistically have a sense of its net worth. I'd say another 2-3 years at least - not because the tech will take that long to adapt, but because that's how long the humans will take to have sufficiently adapted.



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