[dlvhdr]

I don’t get the “just spend more time with AI” argument. Its not a skill, stop trying to make it one. Why should I spend 30 days with it? The only thing that would accomplish is taking the soul and joy out of everything. Everyone just sound like they don’t like coding.

[jsnell]

Of course using AIs is a skill, just like e.g. effectively writing search queries used to be a skill back in the day. When I actually tried getting something done with AI models for the first time, rather than just kicking the tires with the implicit motivation of showing how useless they were, it took way more iterations to get a satisfactory output at the start than a week later.

The kinds of things you'll learn are:

- What's even worth asking for? What categories of requests just won't work, what scope is too large, what kinds of things are going to just be easier to do yourself?

- Just how do you phrase the request, what kind of constraints should you give up front, what kind of things do you need to tell it that should be self-evident but aren't?

- How do you deal with sub-optimal output? Whe do you fix it yourself, when do you get the AI to iterate on it, when do you just throw out the entire sessions and start afresh?

The only way for it to not be a skill would be if how you use an AI either did not matter for the quality output, or if getting better results just a natural talent some people have and some don't. Both of those seem like pretty unrealistic ideas.

I think there's probably a discussion to be had about how deep or transferrable the skill is, but your opening gambit of "it's not a skill, stop trying to make it one" is not a productive starting point for such a discussion.

[mrweasel]

> What's even worth asking for?

That seems to be a struggle for many. A friend of my wife turned 50 and we went to her birthday party. Two speechs and one song was AI generated, two speeches where written by actual humans, guess which should never have been created, let alone performed?

More and more I struggle to see the point of LLMs. I can sort of convince myself that there are niches where LLMs are really useful, but it's getting harder to maintain that illusion. There are cases where AI technologies are truly impressive and transformative, but they are rarely based on a chat interface.

[rini17]

But why would you devote much time and energy trying to massage AI when you can instead apply directly to the problem? With likely more satisfying process and result. You paint it as if that was some prejudice.

[jsnell]

No, I was pretty careful to address only the very specific claim the OP made about how effective AI use is not a skill. If you're reading anything more than that into the comment, I think you're projecting. I really don't care at all whether you or the OP use AIs, and am not trying to convince you of that either way.

[rini17]

My personal experience is that it might be called a skill like learning to use dull knife can be called a skill. I might be mistaken, but I need to see clear process and result. Not lengthy comments like "no it's still useful but you need to approach it deliberately".

And rest assured I don't care about you either(why such tone lol).

[namaria]

I agree, it is absolutely not a skill. LLMs are a black box and the models keep changing under you, and their output can change if you try the exact same input more than once.

People claiming it's a skill should read up on experiments on behavior adaptation to stochastic rewards. Subjects develop elaborate "rain dances" in the belief that they can influence the outcome. Not unlike sports fans superstitions.

[fhd2]

This. If there was some stability in the space, you could empirically develop good practices that probably beat naive practices. But since everything changes every couple of months and since you'll usually want to try different models on an ongoing basis, I found I'm doing just fine with a very small bag of tricks.

Sure, by definition, prompting is a skill. But it's a skill that really isn't hard to learn, and the gap between a beginner and a master is pretty narrow. The real differentiator is understanding the domain you're promoting for deeply, e.g. software development or visual design. Most value comes out of knowing what to ask for, and knowing how to evaluate the results.

[or_am_i]

Analogy would have been correct if prompting didn't influence the output (which I hope you agree is not the case).

And yes, the model keeps changing under you -- much like a horse is changing under a jockey, forcing them to adapt. Or like formula drivers and different car brands.

You can absolutely improve the results by experimenting with prompting, by building a mental mode of what happens inside the "black box", by learning what kinds of context it has/does not have, how (not) to overburden it with instructions etc. etc.

[Xmd5a]

And yet prompts can be optimized.

[AlexeyBrin]

You can optimize a prompt for a particular LLM model and this can be done only through experimentation. If you take your heavily optimized prompt and apply it to a different model there is a good chance you need to start from scratch.

What you need to do every few months/weeks depending of when the last model was released is to reevaluate your bag of tricks.

At some point it becomes a roulette - you try this, you tray that and maybe it works or maybe not ...

[Xmd5a]

Stumbled upon this in another thread:

https://ai-analytics.wharton.upenn.edu/generative-ai-labs/re...

My point still holds that it is optimizable though (https://github.com/zou-group/textgrad, https://arxiv.org/abs/2501.16673)

>Subjects develop elaborate "rain dances" in the belief that they can influence the outcome. Not unlike sports fans superstitions.

Anybody tuning neural weights by hand would feel like this.

[latexr]

> Everyone just sound like they don’t like coding.

It’s no secret that a lot of people (I’d like having an accurate percentage) got into coding because of the money. When you view it from that perspective everything becomes clearer: those people don’t care for the craft or correctness. They don’t understand or care when something is wrong and it’s in their best interest to convince themselves and everyone else that AI is as good or better than any expert programmer because it means they themselves don’t need to improve or care more than what they already don’t, they can just concentrate on the getting rich part.

There are experts (programmers or otherwise) who use these tools as guidelines and always verify the output. But too often they defend LLMs as unambiguously good because they fail to understand the overwhelming majority of humans aren’t experts or sufficiently critical of whatever they read, taking whatever the LLM spits as gospel. Which is what makes them dangerous.

[NitpickLawyer]

> Its not a skill, stop trying to make it one.

Using it efficiently is absolutely a skill. Just like google-fu is a skill. Or reading fast / skimming is a skill. Or like working with others is a skill. And so on and so on.

[ninetyninenine]

Agreed it’s a skill in the same way walking is a skill.

[j-bos]

I'd using bicycling as the analogy, some people never learn and thus don't understand the gains it provides.

[ninetyninenine]

Yeah. It’s a skill. I used walking to basically say it’s like a universal skill that’s deadly easy to learn. So easy that it doesn't even feel like a skill.

Bicycling is slightly harder than walking.

[mr_mitm]

So strange, I haven't had this much fun at coding in a long time. It's amazing.

[regularjack]

Why is it strange? Different people enjoy different things. Seems normal to me

[simonw]

It's a skill. The more time (and intentional practice) you invest in it the better you'll get at it.