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? 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.
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.
That’s exactly the point from people who are making systemic critiques of the “AI” craze. LLMs by their very nature, encourage people to become lazy. I’m not worried about a machine that’s constantly wrong, depending on the application, I can control for that. I am worried about a machine that is almost always right. That’s really dangerous, because it lulls its users into a false sense of confidence and security.
There’s a saying I heard from an old sys-admin once, “It’s better to be broadly right, than precisely wrong.” Modern LLMs are a perfect example of the latter, they will get 95% of a task correct, but then mess up that last 5%. But it messes up that 5% in a way that is very subtle and convincing, and requires somebody with deep, technical knowledge to catch and mitigate. That fact, combined with how lazy people are incentivized to be when using them, is the deadly combo imo.
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? 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.
That’s exactly the point from people who are making systemic critiques of the “AI” craze. LLMs by their very nature, encourage people to become lazy. I’m not worried about a machine that’s constantly wrong, depending on the application, I can control for that. I am worried about a machine that is almost always right. That’s really dangerous, because it lulls its users into a false sense of confidence and security.
There’s a saying I heard from an old sys-admin once, “It’s better to be broadly right, than precisely wrong.” Modern LLMs are a perfect example of the latter, they will get 95% of a task correct, but then mess up that last 5%. But it messes up that 5% in a way that is very subtle and convincing, and requires somebody with deep, technical knowledge to catch and mitigate. That fact, combined with how lazy people are incentivized to be when using them, is the deadly combo imo.