To play devils advocate, why do we have to layout a simple task in PAINSTAKING DETAIL to an AI model which is "PHD LEVEL" and going to take our jobs in 6-12 months?

Why am I still holding its hand like it has the intellect and experience of a new-hire intern that's coded one project in college?

I would never expect to have to layout every detail about "how to write code" to someone I hired to code on my team, at the SWEII and above level. (I.e, sub-senior but beyond junior)

In fact, often times backlog items are "fix bug in x where y is happening" or "add instrumentation to X so that we can see why it's crashing at runtime".

I find that generally it does alright picking up the style of what exists on its own, so this is more important if it's writing something completely from scratch.

I think also "how to write code" is a matter of taste. e.g. in many ways I think I and a Laravel or Rails developer would each think that the other person's code is bad. e.g. as a small-ish thing, I think test-driven development sounds like a massive waste of time, but type-driven development is a huge productivity multiplier and makes the code a lot clearer. I'm sure that I have massive disagreements with e.g. the Go maintainers about what is straightforward.

> PHD LEVEL

It is PhD level. Most PhD students write awful code that's worse than AI.

Because the models aren't PhD level and aren't going to take our jobs in 6-12 months.

That's hype. If you want to use these things effectively you need to ignore the hype and focus on what they can actually do.

Don't worry about devil's advocate, if < 100 words feels like a gargantuan amount of documentation effort ("PAINSTAKING DETAIL"), well, there are certain stereotypes about developers (not) writing comments or documentation that come to mind. Whoever coined the term "prompt engineering" may have the last laugh (before the robots take over) after all.