AI works better if you ask it to be a Star Trek character
I’m leaving the hed as-is per protocol, but the larger story here seems to be we’ve already hit the point where LLMs produce better prompts for other LLMs than human prompt engineers do.
This is not in my wheelhouse but feels like something of a marker being laid down far sooner than anyone was publicly expressing. The fact itself isn’t all that surprising since we don’t think in weights, and this is so far domain specific, but people were unironically talking about prompt engineering being a field with a promising future well into this year.
I use ChatGPT daily for work. Much of what I do is rewriting government press releases for a trade publication, so I’ll often have ChatGPT paraphrase (literally paraphrase: ) paragraphs which I’ll then paste into my working document after comparing to the original and making sure something festive didn’t show up in translation.
Sometimes, I have to say “this was a terrible result with almost no deviation from the original and try again,” at which point I get the result I’m looking for.
As plagiarism goes, no one’s going to rake you over the coals for a press release, written to be run verbatim. And within that subset, government releases are literally public domain. Still, I’ve got these fucking journalism ethics.
So, I’ve got my starting text (I’ve not tried doing a full story in 4o yet) from which I’ll write my version knowing that if I do end up changing “enhanced” to “improved” where the latter is the original in the release, I’m agreeing with an editorial decision, not plagiarizing.
For what I do, it’s a godsend. For now. But because I can define the steps and reasoning, an LLM can as well, and I see no reason the linked article is wrong in assuming that version would be better than what I do.
From there, I add quotes, usually about where they were in the release but stripped of self-congratulatory bullshit (remove all references in quotes to figures not quoted themselves in the story and recast with unquoted intro to match the verb form used in the predicate, where the quote picks up would, frankly, get you 90% of the way there) and compile links (For all proper nouns encountered, search the Web to find the most recent result from the body issuing the release; if none found, look on other ‘.gov’ sites; if none found, look for ‘.org’ links; if none, stop attempting to link and move on to next proper noun).
It sounds like all this (and more!) could be done by LLM’s today, relegating me to the role of copyeditor (not the briar patch!). Cool. No one’s reading my stories about HVDC transmission lines for my dry wit, so with a proper line of editing, the copy would be just as readable, and I’d have more time to fact-check things or find a deeper resource to add context.
But then how much more quickly do we get to a third layer of machine instructions that takes over everything that can be turned into an algorithm in my new role? At a certain point, all I have to offer that seems unattainable for LLMs (due to different heuristics and garbage training data) even in the medium term is news judgment, which isn’t exactly a high-demand skill.
This development worries me far more than anything I’ve read about LLM advancements in quite some time.
