Comment on AI agents wrong ~70% of time: Carnegie Mellon study
sugar_in_your_tea@sh.itjust.works 11 hours agoExactly! LLMs are useful when used properly, and terrible when not used properly, like any other tool. Here are some things they’re great at:
Some things it’s terrible at:
I use LLMs a handful of times a week, and pretty much only when I’m stuck and need a kick in a new (hopefully right) direction.
I will say I’ve found LLM useful for code writing but I’m not coding anything real at work. Just bullshit like SQL queries or Excel macro scripts or Power Automate crap.
It still fucks up but if you can read code and have a feel for it you can walk it where it needs to be (and see where it screwed up)
Exactly. Vibe coding is bad, but generating code for something you don’t touch often but can absolutely understand is totally fine. I’ve used it to generate SQL queries for relatively odd cases, such as CTEs for improving performance for large queries with common sub-queries. I always forget the syntax, and LLMs are great at generating something reasonable that I can tweak for my tables.
I always forget the syntax
Me with literally everything code I touch always and forever.
spankmonkey@lemmy.world 11 hours ago
I used to be able to use Google and other search engines to do these things before they went to shit in the pursuit of AI integration.
sugar_in_your_tea@sh.itjust.works 10 hours ago
Google search was pretty bad at each of those, even when it was good. Finding new keywords to use is especially difficult the more niche your area of search is, and I’ve spent hours trying different combinations until I found a handful of specific keywords that worked.
Likewise, search is bad for getting a broad summary, unless someone has bothered to write it on a blog. But most information goes way too deep and you still need multiple sources to get there.
Fact lookup is one the better uses for search, but again, I usually need to remember which source had what I wanted, whereas the LLM can usually pull it out for me.
I use traditional search most of the time (usually DuckDuckGo), and LLMs if I think it’ll be more effective. We have some local models at work that I use, and they’re pretty helpful most of the time.
jjjalljs@ttrpg.network 8 hours ago
It is absolutely stupid, stupid to the tune of “you shouldn’t be a decision maker”, to think an LLM is a better use for “getting a quick intro to an unfamiliar topic” than reading an actual intro on an unfamiliar topic. For most topics, wikipedia is right there, complete with sources. For obscure things, an LLM is just going to lie to you.
As for “looking up facts when you have trouble remembering it”, using the lie machine is a terrible idea. It’s going to say something plausible, and you tautologically are not in a position to verify it. And, as above, you’d be better off finding a reputable source. If I type in “how do i strip whitespace in python?” an LLM could very well say “it’s your_string.strip()”. That’s wrong. Just send me to the fucking official docs.
There are probably edge or special cases, but for general search on the web? LLMs are worse than search.
sugar_in_your_tea@sh.itjust.works 6 hours ago
The LLM helps me know what to look for in order to find that unfamiliar topic.
For example, I was tasked to support a file format that’s common in a very niche field and never used elsewhere, and unfortunately shares an extension with a very common file format, so searching for useful data was nearly impossible. So I asked the LLM for details about the format and applications of it, provided what I knew, and it spat out a bunch of keywords that I then used to look up more accurate information about that file format. I only trusted the LLM output to the extent of finding related, industry-specific terms to search up better information.
Likewise, when looking for libraries for a coding project, none really stood out, so I asked the LLM to compare the popular libraries for solving a given problem. The LLM spat out a bunch of details that were easy to verify (and some were inaccurate), which helped me narrow what I looked for in that library, and the end result was that my search was done in like 30 min (about 5 min dealing w/ LLM, and 25 min checking the projects and reading a couple blog posts comparing some of the libraries the LLM referred to).
I think this use case is a fantastic use of LLMs, since they’re really good at generating text related to a query.
I absolutely am though. If I am merely having trouble recalling a specific fact, asking the LLM to generate it is pretty reasonable. There are a ton of cases where I’ll know the right answer when I see it, like it’s on the tip of my tongue but I’m having trouble materializing it. The LLM might spit out two wrong answers along w/ the right one, but it’s easy to recognize which is the right one.
I’m not going to ask it facts that I know I don’t know (e.g. some historical figure’s birth or death date), that’s just asking for trouble. But I’ll ask it facts that I know that I know, I’m just having trouble recalling.
The right use of LLMs, IMO, is to generate text related to a topic to help facilitate research. It’s not great at doing the research though, but it is good at helping to formulate better search terms or generate some text to start from for whatever task.
I agree, it’s not great for general search. It’s great for turning a nebulous question into better search terms.
spankmonkey@lemmy.world 10 hours ago
No search engine or AI will be great with vague descriptions of niche subjects because by definition niche subjects are too uncommon to have a common pattern of ‘close enough’.
sugar_in_your_tea@sh.itjust.works 9 hours ago
Which is why I use LLMs to generate keywords for niche subjects. LLMs are pretty good at throwing out a lot of related terminology, which I can use to find the actually relevant, niche information.
I wouldn’t use one to learn about a niche subject, but I would use one to help me get familiar w/ the domain to find better resources to learn about it.