If the tool needs you to check up on it and fix its work, it's a bad tool.
If the tool needs you to check up on it and fix its work, it's a bad tool.
It’s not that binary imo. It can still be extremely useful and save a ton of time if it does 90% of the work and you fix the last 10%. Hardly a bad tool.
It’s only a bad tool if you spent more time fixing the results than building it yourself, which sometimes used to be the case for LLMs but is happening less and less as they get more capable.
I agree that there are domains for which 90% good is very, very useful. But 99% isn't always better. In some limited domains, it's actually worse.
Humans don't get it right 100% or the time.
This isn't about whether AI is statistically safer, it's actually about the user experience of AI: If we can provide the same guidance without lulling a human backup into complacency, we will have an excellent augmented capability.
All tools have failure modes and truthfully you always have to check the tool's work (which is your work). But being a master craftsman is knowing all the nuances behind your tools, where they work, and more importantly where they don't work.
That said, I think that also highlights the issue with LLMs and most AI. Their failure modes are inconsistent and difficult to verify. Even with agents and unit tests you still have to verify and it isn't easy. Most software bugs are created from subtle things, often which compound. Which both those things are the greatest weaknesses of LLMs: nuance and compounding effects.
So I still think they aren't great tools, but I do think they can be useful. But that also doesn't mean it isn't common for people to use them well outside the bounds of where they are generally useful. It'll be fine a lot of times, but the problem is that it is like an alcohol fire[0]; you don't know what's on fire because it is invisible. Which, after all, isn't that the hardest part of programming? Figuring out where the fire is?