Post #4657655 · Elektrine

2026-09-02 05:20 UTC

Good point. I probably should have stated “using LLMs for technical tasks that anyone cares about the results of”. I’m not going to claim LLMs are trustworthy, but I am saying that there are ways to ask things that either reduce the odds of it giving you false things or actively cause it to provide wrong answers with misstated or hallucinated context. People who know what they’re doing can get better results out of them. This doesn’t stop them from being the equivalent of a nepo-hire intern, but it could be said to change whether they’re a malicious, apathetic, or semi-eager nepo-hire intern. And one of the big risks with LLMs is staff who can recognise bullshit or questionable results being replaced with staff who unquestioningly accept whatever results they get. And people reading an “AI summary” of something and assuming it’s actually accurate.

Replies (2)

@porous_grey_matter@lemmy.ml 2026-09-02 06:01

People who know what they’re doing can get better results out of them. I don’t think that’s true in the way you appear to mean (sorry if I misunderstood). People who are already experts on the subject matter might be able to use better keywords and discard hallucinatory material quicker, but I don’t believe that you can generically “be good at prompting” outside of an area where you have substantial domain knowledge. And the only way to learn to recognise bullshit involves not using LLMs or other automated tools and working problems out for yourself, not to mention that they’re an “intern” who actually costs the same in computing power as two senior engineers’ salary.

Open ##4658369
@Lettuceeatlettuce@lemmy.ml 2026-09-04 05:41

Sure, how and what you ask does make a difference, especially on lower power models. But it’s generally not really significant, like learning how to write more effective prompts takes maybe a few hours of total time to learn? Honestly, probably an hour at most for 95% of people using LLMs in a corporate environment. The irony of all this, is that one of the supposed biggest advantages of LLMs is that you can just talk to them with natural language in a conversational format. The more people have to use special rules of conversation, format their prompts in specific ways, take advantage or avoid subtleties of the model’s preferred syntactic style, etc. The more LLMs become a technical tool that can only produce high quality results if you use it in very specific, skilled ways.

Open ##4669507