No matter how good AI gets we will never be in a situation where a person with poor communication skills will be able to use it as effectively as someone who's communication skills are razor sharp.
But the examples you've posted have nothing to do with communication skills, they're just hacks to get particular tools to work better for you, and those will change whenever the next model/service decides to do things differently.
I'm generally skeptical of Simon's specific line of argument here, but I'm inclined to agree with the point about communication skill.
In particular, the idea of saying something like "use red/green TDD" is an expression of communication skill (and also, of course, awareness of software methodology jargon).
Ehhh, I don't know. "Communication" is for sapients. I'd call that "knowing the right keywords".
And if the hype is right, why would you need to know any of them? I've seen people unironically suggest telling the LLM to "write good code", which seems even easier.
I sympathize with your view on a philosophical level, but the consequence is really a meaningless semantic argument. The point is that prompting the AI with words that you'd actually use when asking a human to perform the task, generally works better than trying to "guess the password" that will magically get optimum performance out of the AI.
Telling an intern to care about code quality might actually cause an intern who hasn't been caring about code quality to care a little bit more. But it isn't going to help the intern understand the intended purpose of the software.
I'm not making a semantic argument, I'm making a practical one.
> prompting the AI with words that you'd actually use when asking a human to perform the task, generally works better
Ok, but why would you assume that would remain true? There's no reason it should.
As AI starts training on code made by AI, you're going to get feedback loops as more and more of the training data is going to be structured alike and the older handwritten code starts going stale.
If you're not writing the code and you don't care about the structure, why would you ever need to learn any of the jargon? You'd just copy and paste prompts out of Github until it works or just say "hey Alexa, make me an app like this other app".
Why do you bother with all this discussion? Like, I get it the first x times for some low x, it's fun to have the discussion. But after a while, aren't you just tired of the people who keep pushing back? You are right, they are wrong. It's obvious to anyone who has put the effort in.
It's also useful for figuring out what I think and how best to express that. Sometimes I get really great replies too - I compared ethical LLM objections to veganism today on Lobste.rs and got a superb reply explaining why the comparison doesn't hold: https://lobste.rs/s/cmsfbu/don_t_fall_into_anti_ai_hype#c_oc...
Yes and no. Knowing the terminology is a short-cut to make the LLM use the correct part of its "brain".
Like when working with video, if you use "timecode" instead of "timestamp", it'll use the video production part of the vector memory more. Video production people always talk about "timecodes", not "timestamps".
You can also explain the idea of red/green testing the long way without mentioning any of the keywords. It might work, but just knowing you can say "use red/green testing" is a magic shortcut to the correct result.
Thus: working with LLMs is a skill, but also an ever-changing skill.
