4 ms·
I agree with most of this, but my one qualm is the notion that LLMs "are particularly good at generating ideas." It's fair enough that you can discard any bad
by nerevarthelame 6mo ago
I agree with most of this, but my one qualm is the notion that LLMs "are particularly good at generating ideas."
It's fair enough that you can discard any bad ideas they generate. But by design, the recommendations will be average, bland, mainstream, and mostly devoid of nuance. I wouldn't encourage anyone to use LLMs to generate ideas if you're trying to create interesting or novel ideas.
- dummydummy1234 6mo agoI have found the one of the better use cases of llms to be a rubber duck. Explaining a design, problem, etc and trying to find solutions is extremely useful. I can bring novelty, what I often want from the LLM is a better understanding of the edge cases that I may run into, and possible solutions.
- monkaiju 6mo agoI always find folks bringing up rubber ducking as a thing LLMs are good at to be misguided. IMO, what defines rubber ducking as a concept is that it is just the developer explaining what their doing to themselves. Not to another person, and not to a thing pretending to be a person. If you have a "two way" or "conversational" debugging/designing experience it isnt rubber ducking, its just normal design/debugging. The moment I bring in a conversational element, I want a being that actually has problem comprehension and creativity which an LLM by definition does not.
- deleted 6mo ago[deleted]
- dexwiz 6mo agoSometimes people just need something else to tell them their ideas are valid. Validation is a core principle of therapeutic care. Procrastination is tightly linked to fear of a negative outcome. LLMs can help with both of these. They can validate ideas in the now which can help overcome some of that anxiety. Unfortunately they can also validate some really bad ideas.
- satvikpendem 6mo agoSometimes I don't want creativity though, I'm just not familiar enough with the solution space and I use the LLM as a sort of gradient descent simulator to the right solution to my problem (the LLM which itself used gradient descent when trained, meta, I know). I am not looking for wholly new solutions, just one that fits the problem the best, just as one could Google that information but LLMs save even that searching time.
- bandrami 6mo ago> I'm just not familiar enough with the solution space Neither is the LLM
- satvikpendem 6mo agoOftentimes it is though, good enough for my purposes.
- bandrami 6mo agoIf you're not familiar with the problem space, by definition you don't know whether or not that's the case. The problem spaces I do know well, I know the LLM isn't good at it, so why would I assume it's better at spaces I don't know?
- satvikpendem 6mo agoI said familiar enough, not familiar. For example, let's say I'm building an app I know needs caching, the LLM is very good at telling me what types of caching to use, what libraries to use for each type, and so on, for which I can do more research if I really want to know specifically what the best library out of all the rest are, but oftentimes its top suggestion is, like I said, good enough for my purpose of e.g. caching.
- bandrami 6mo agoI still don't get what you're saying. If you possess enough information to accurately judge the LLM's suggestions you possess enough information to decide on your own. There's not really a way around that.
- jrowen 6mo agoI feel I've had the most success with treating it like another developer. One that has specific strengths (reference/checklists/scanning) and weaknesses (big picture/creativity). But definitely bouncing actual questions that I would say to a person off it.
- Waterluvian 6mo agoMaybe it’s just a semantic distinction, which, sure. I guess I’d just call it research? It’s basically the “I’m reading blogs, repos, issue trackers, api docs etc. to get a feel for the problem space” step of meaningful engineering. But I definitely reach for a clear and concise way to describe that my brain and fingers are a firewall between the LLM and my code/workspace. I’m using it to help frame my thinking but I’m the one making the decisions. And I’m intentionally keeping context in my brain, not the LLM, by not exposing my workspace to it.
- WCSTombs 6mo agoAbsolutely, the whole point of the rubber duck is that it's inanimate. The act of talking to the rubber duck makes you first of all describe your problem in words, and secondly hear (or read) it back and reprocess it in a slightly different way. It's a completely free way to use more parts of your brain when you need to. LLMs are a non-free way for you to make use of less of your brain. It seems to me that these are not the same thing.
- __mharrison__ 6mo agoMy understanding was that rubber ducking was using a different portion of your brain by speaking the words. The same discovery often happens when you explain a problem to a coworker and midway through the explanation you say "nvm, I know what I did wrong"
- globular-toast 6mo agoDo you not know any people who can help? Suddenly realised how lonely this sounds.
- apsurd 6mo agoCoordinating with people is hard and only gets harder as you live. And actually, finding someone that is earnestly receptive to hearing you pitch your half-baked startup ideas (just an example) and is in some capacity qualified to be at all helpful, is uhhh, not easy.
- globular-toast 6mo agoReally? Sometimes I think I'm not very social, then I read something like this. Don't you have any friends? Colleagues? Maybe that's the problem you need to solve rather than sitting in a room burning energy for endless token streams with LLMs that anyone has access to?
- apsurd 6mo agolol. nobody is proposing this "well if not friends, then...". Appreciate your concern. I am fine. This is for Internet posterity: thought-partnering with AI does not in fact make you a sorry socially inept loser that needs globular-toast to come in and help you dial that helpline. Also: one's friends do not, in reality want to thought-partner about work issues, esoteric hobbies, and that million dollar idea. Also: these friends, every and any one of them it seems, will not in fact speak the word of God into your ear as manifest insight for said work issue, million dollar idea, and so forth.
- apsurd 6mo agoAh, I couldn't help myself practice my creative writing in the other reply. This reply is more constructive: Both LLM based rubber-ducking and human discussions seem like a win win. I see no reason to jump to labeling unhealthy social connections just for pairing with LLMs.
- j45 6mo agoAsking the LLM better will return better than average and bland and mainstream results.
- paulryanrogers 6mo agoHow does one ask better? Does better vary per model?
- j45 6mo agoYes, its context based. It's like asking a coworker. Providing too little information, or too much context can give different responses. Try asking the model to not provide it's most common or average answer. Been using it this way for 2, almost 3 years.
- contagiousflow 6mo agoWhy would they return "better" results?
- j45 6mo agoBecause AI is not a search engine. It does not return the best search result every time. What it considers best, is what occurs most often, which can be the most average answers. Unless the service is tuned for search (perplexity, or google itself for example), others will not provide as complete an answer. How well we ask can make all the difference. It's like asking a coworker. Providing too little information, or too much context can give different responses. Try asking the model to not provide it's most common or average answer. Been using it this way for 2, almost 3 years.
- dgxyz 6mo agoAll LLM output is always dry as fuck quite frankly. At all levels from ideas and concepts through to the actual copy. And that’s dotted with pure excrement. I think the only reason it’s seen as good anywhere is there are a lot of tasteless and talentless people who can pretend they created whatever was curled out. This goes for code as well. If I offend anyone I will not be apologising for it.
- gavmor 6mo agoAgreed! No LLM is producing Pynchon, Calvino, Borges, Castaneda, Le Guin, Vonnegut.
- darth_aardvark 6mo ago> curled out This is the kind of understated yet thoroughly disgusting imagery an LLM couldn't come up with on its own, great example.
- dgxyz 6mo agoThank you :)
- BoredomIsFun 6mo ago> If I offend anyone I will not be apologising for it. What you said is simply counterfactual, so no reason to be offended.
- paulpauper 6mo agoLLMs can come sometimes up with novel or non-obvious insights...or just regurgitate google-like results.
- deleted 6mo ago[deleted]
- NewsaHackO 6mo agoYes, I didn't get this portion at all. I feel as though letting an LLM brainstorm ideas for you would be worse in externally framing your thoughts than letting it write/proofread for you. If you pick one idea out of the 10 presented by the LLM, you are still confining yourself to the intersection of what the LLM thinks is important and what you think is important, because then you can never "generate" a thought that the LLM hasn't presented.
- furyofantares 6mo agoI'm torn. I sometimes use them when I'm stuck on something, trying to brainstorm. The ideas are always garbage, but sometimes there is a hint of something in one of them that gets me started in a good direction. Sometimes, though, I feel MORE stuck after seeing a wall of bad ideas. I don't know how to weigh this. I wasn't making progress to begin with, so does "more stuck" even make sense? I guess I must feel it's slightly useful overall as I still do it.
- raincole 6mo agoMainstream ideas are often good. That's why they're mainstream. Being different for being different isn't a virtue. That being said I don't think LLMs are idea generators either. They're common sense spitters, which many people desperately need.
- jrowen 6mo agoI think it's just a confusing use of the term "generating." It's thinking of the LLM as a thesaurus. You actually generate the real idea -- and formulate the problem -- it's good at enumerating potential solutions that might inspire you.
- jimbokun 6mo agoThat can be very valuable.
- samusiam 6mo ago"by design, the recommendations will be average" This couldn't be more wrong. The simplest refutation is just to point out that there are temperature and top-k settings, which by design, generate tokens (and by extension, ideas) that are less probable given the inputs.
- deleted 6mo ago[deleted]
- ozozozd 6mo agoHaving to fix the LLM’s recommended solution is a good exercise though. It’s like being a smart-ass for the right reasons, without any social consequences.
- KolibriFly 6mo agoSo maybe the framing is: LLMs are good at mapping the landscape, but not at discovering new continents