4 ms·
> LLMs are extremely handy, at least when they don't just randomly hallucinate I work in tech and it’s my hobby, so that’s what a lot of my googling goes towar
by dartos 2y ago
> LLMs are extremely handy, at least when they don't just randomly hallucinate
I work in tech and it’s my hobby, so that’s what a lot of my googling goes towards.
LLMs hallucinate almost every time I ask them anything too specific, which at this point in my career is all I’m really looking for.
The time it takes for me to realize an llm is wrong is usually not too bad, but it’s still time I could’ve saved by googling (or whatever trad search) for the docs or manual.
I really wish they were useful, but at least for my tasks they’re just a waste of time.
I really like them for quickly generating descriptions for my dnd settings, but even then they sound samey if I use them too much.
Obviously they’d sound samey if I made up 20 at once too, but at that point I’m not really being helped or enhanced by using an LLM, it’s just faster at writing than I am.
- Workaccount2 2y agoI don't mean this as a slight, just an observation I have seen many times - people who struggle with utility from SOTA LLM's tend to not have spent enough time with them to feel out good prompting. In the same way that there is a skill for googling information, there is a skill for teasing consistent good responses from LLM's.
- danielbln 2y agoPeople also continue to use them as knowledge databases, despite that not being where they shine. Give enough context into the model (descriptions, code, documentation, ideas, examples) and have a dialog, that's where these strong LLMs really shine.
- dartos 2y agoSummarizing, doc qa, and unstructured text ingestion are the killer features I’ve seen. The 3rd one still being quite involved, but leaps and bounds easier than 5 years ago.
- dartos 2y agoWhy spend my time teasing and coaxing information out of a system which absolutely does make up nonsense when I can just read the manual? I spent 2023 developing LLM powered chatbots with people who, purportedly, were very good at prompting, but never saw any better output than what I got for the tasks I’m interested in. I think the “you need to get good at prompting” idea is very shallow. There’s really not much to learn about prompting. It’s all hacks and anecdotes which could change drastically from model to model. None of which, from what I’ve seen, makes up for the limitations of LLM no matter how many times I try adding “your job depends on Formatting this correctly “ or reordering my prompt so that more relevant information is later, etc Prompt engineering has improved RAG pipelines I’ve worked on though, just not anything in the realm of comprehension or planning of any amount of real complexity.