LLMs are like a multitool, they can do lots of easy things mostly fine as long as it is not complicated and doesn't need to be exactly right. But they are being promoted as a whole toolkit as if they are able to be used to do the same work as effectively as a hammer, power drill, table saw, vise, and wrench.

than reading an actual intro on an unfamiliar topic

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.

It’s going to say something plausible, and you tautologically are not in a position to verify it.

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.

general search on the web?

I agree, it's not great for general search. It's great for turning a nebulous question into better search terms.

than reading an actual intro on an unfamiliar topic

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.

It’s going to say something plausible, and you tautologically are not in a position to verify it.

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.

general search on the web?

I agree, it's not great for general search. It's great for turning a nebulous question into better search terms.

I tried to dictate some documents recently without paying the big bucks for specialized software, and was surprised just how bad Google and Microsoft's speech recognition still is. Then I tried getting Word to transcribe some audio talks I had recorded, and that resulted in unreadable stuff with punctuation in all the wrong places. You could just about make out what it meant to say, so I tried asking various LLMs to tidy it up. That resulted in readable stuff that was largely made up and wrong, which also left out large chunks of the source material. In the end I just had to transcribe it all by hand.

It surprised me that these AI-ish products are still unable to transcribe speech coherently or tidy up a messy document without changing the meaning.

It's a bit frustrating that finding these tools useful is so often met with it can't be useful for that, when it definitely is.

More than any other tool in history LLMs have a huge dose of luck involved and a learning curve on how to ask the right things the right way. And those method change and differ between models too.

One word of caution with AI searxh is that it's weirdly vulnerable to SEO.

If you search for "best X for Y" and a company has an article on their blog about how their product solves a problem the AI can definitely summarize that into a "users don't like that foolib because of ...". At least that's been my experience looking for software vendors.

Were you prone to this weird leaps of logic before your brain was fried by talking to LLMs, or did you start being a fan of talking to LLMs because your ability to logic was...well...that?

I can't believe how absolutely silly a lot of you sound with this.

LLM is a tool. It's output is dependent on the input. If that's the quality of answer you're getting, then it's a user error. I guarantee you that LLM answers for many problems are definitely adequate.

It's like if a carpenter said the cabinets turned out shit because his hammer only produces crap.

Also another person commented that seen the pattern you also see means we're psychotic.

All I'm trying to suggest is Lemmy is getting seriously manipulated by the media attitude towards LLMs and these comments I feel really highlight that.

and? we can understand 256 where AI can't, that's the point.

About 0.02

For me as a software developer the accuracy is more in the 95%+ range.

On one hand the built in copilot chat widget in Intellij basically replaces a lot my google queries.

On the other hand it is rather fucking good at executing some rewrites that is a fucking chore to do manually, but can easily be done by copilot.

Imagine you have a script that initializes your DB with some test data. You have an Insert into statement with lots of columns and rows so

Inser into (column1,....,column n)
Values row1,
Row 2
Row n

Addig a new column with test data for each row is a PITA, but copilot handles it without issue.

Similarly when writing unit tests you do a lot of edge case testing which is a bunch of almost same looking tests with maybe one variable changing, at most you write one of those tests, then copilot will auto generate the rest after you name the next unit test, pretty good at guessing what you want to do in that test, at least with my naming scheme.

So yeah, it's way overrated for many-many things, but for programming it's a pretty awesome productivity tool.

Oh sure, caution is always warranted w/ LLMs. But when it works, it can save a ton of time.

So the chances of it being right ten times in a row are 2%.

Jan Refiner is up there for me.

I think this comment made me finally understand the AI hate circlejerk on lemmy. If you have no clue how LLMs work and you have no idea where "AI" is coming from, it just looks like another crappy product that was thrown on the market half-ready. I guess you can only appreciate the absolutely incredible development of LLMs (and AI in general) that happened during the last ~5 years if you can actually see it in the first place.

You see, I wanted to be petty and do another dismissive reply, but instead I fed our convo to copilot and asked it to explain, here you go, as you can see I have previously used it for coding tasks, so I didn't feed it any extra info, so there you go, even copilot can understand the huge "leap" I made in logic. goddamn the sweet taste of irony.

Copilot reply:

Certainly! Here’s an explanation Person B could consider:

The implied logic in Person A’s argument is that if you distrust code written by Copilot (or any AI tool) simply because it wasn’t written by you, then by the same reasoning, you should also distrust code written by junior developers, since that code also isn’t written by you and may have mistakes or lack experience.

However, in real-world software development, teams regularly review, test, and maintain code written by others—including juniors, seniors, and even AI tools. The quality of code depends on review processes, testing, and collaboration, not just on who wrote it. Dismissing Copilot-generated code outright is similar to dismissing the contributions of junior developers, which isn’t practical or productive in a collaborative environment.

How do I set up event driven document ingestion from OneDrive located on an Azure tenant to Amazon DocumentDB? Ingestion must be near-realtime, durable, and have some form of DLQ.

No the chances of being wrong 10x in a row are 2%. So the chances of being at least right once are 98%.

I was 0/6 on various trials of AI for Rust over the past 6 months, then I caught a success. Turns out, I was asking it to use a difficult library - I can't make the thing I want work in that library either (library docs say it's possible, but...) when I posed a more open ended request without specifying the library to use, it succeeded - after a fashion. It will give you code with cargo build errors, I copy-paste the error back to it like "address: <pasted error message>" and a bit more than half of the time it is able to respond with a working fix.