I actually think that good prompting MOSTLY comes from good writing skills in general. Being able to more clearly state things to an agent, knowing what pieces of context are entirely unnecessary and which are important, having a larger vocabulary helps too.

Of course, there are other areas that can improve model output (Direction rather than open-ended assistance requests, using keywords + plugins that help, the "your output should include: " style prompting).

A few of us run almost the same exact setup at my shop (Base Claude Code w/ SuperPowers + a context repository) and the models are somewhat unhelpful to some, and give meaningful output to others. The only correlation I notice is that their prompts are no-good. Not from a meta "prompt" engineering standpoint, but from a general English 101 standpoint.

"dudde no i wanted the function to return 3 things. not like that. do it again"

VS something like

"Modify the "renderThreeVars()" function signature to accept another variable called "z" and add it to the return statement at line 64."

Obvious exaggeration, but you get the point.

As part of a previous job I needed to audit internal AI usage from a largely non-technical employee population.

The prompts were, predictably, really bad. Broken English, sentence fragments, vague requests, lack of context. Yet somehow, the users always got the answer they were looking for. It might have taken a few extra turns with questions from the model, but the end result was the same.

It's humbling, but a flowery, carefully crafted prompt is at best slightly more efficient than a "CAN A DOG BE EATIN SUN FLOWER SEED?" peasant prompt.

Nah, you might be confusing prompt engineering with having domain knowledge. :)

I feel the same way about "prompt engineering" as I feel regarding the term "parkour" - you know, running and jumping on stuff.

Are people really putting on their resumes that they are capable of reading and writing and appropriately defining and limiting context? That's all prompt engineering is - it's being able to communicate effectively and elucidate your objectives.

Congratulations to all you English majors out there, you're about to make $350K/year.

What's clear is that there is a lot of hype around LLM and people who were previously valued for their IC are now in the business of shilling.

RL has basically killed prompt engineering. You still need to provide the right context and process, but how you communicate with them beyond that is no longer so important.

> Or is the idea that what you write to them and how you use them doesn't matter, it's all up to the model/harness?

Yes, it is. Source: had models inplement complex things from scratch and bullshit regardless of whether it was a one-line prompt or a detailed "SOTA witchcraft magic spells that are guaranteed to work"

It's not really a skill. The models are at this point smarter than you are, so the idea that you can prompt them "better" is laughable really when discussing frontier models.

It's like imagining you could "prompt" Richard Feynman to be smarter at Physics.

That is, for 99.9% of engineers, if you want the model to do a code review of your project, the best solution is to just ask Fable, "Hey Fable, do a code review of this project." Throwing in extra text like "think like a senior engineer", "ensure you focus on DRY principles, KISS, self documenting code, etc", doesn't make a difference.

These sorts of tricks used to work with dumber models, but now, like I said before, it's like thinking you can prompt Linus Torvalds into writing better C than he already can do.

Sure but lets not pretend this is engineering.

Definitely. But the special knowledge how to talk to a certain model is usually not worth it.

Being able to write clear, always is.