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I disagree with several of these, and the whole thing does not seem very well-conceived, based on an "incorrect" notion of what AI "should" do to be considered
by tmnvdb 2y ago
I disagree with several of these, and the whole thing does not seem very well-conceived, based on an "incorrect" notion of what AI "should" do to be considered impressive.
> LLMs only reliably know what you just told them, don't rely on training data
This depends a lot on the model but i've been using 4o for all kinds of information retrieval and found it to be generally reliable. At least not much worse than the general internet. You can ask it for sources and of course you should not use it as an authority, but it can often be a very good way to quickly find out a fact. You do need to developed a feeling for the kind of thing it will know realiably and the kind of thing that will cause it to start halucinating (a bit like some of your co-workers).
> LLMs cannot write for you
I disagree, LLMs can write small blocks of text very well. But there is an art to using it. Don't try to create too much at once. I often find it works better then I give less input. If you list a bunch of things it needs to include, it tends the result reads like a student trying to include all the buzzwords.
> LLMs can help a human perform tasks, they cannot replace a human
I don't think anybody claims otherwise for current public models.
> Have the LLM do as little as possible
You need to learn what LLMs do well, and then use it for that. The idea that it is most efficient to program everything by hand as much as possible does not match my experience. Writing boilerplate code of under 50 lines of code or so is something current models already do very well and very quickly. I usually just try to generate it and if it does not work I write it by hand.
Finally, LLMs now take video and audio. We use Gemini to write meeting notes from Google meet and they tend to be very high quality, a lot better then what a random person taking notes usually produces. So the models are not text-only.
- simonw 2y agoWhen you say "You can ask it for sources" are you talking about the GPT-4o model or the ChatGPT feature where it can search the internet on your behalf? ChatGPT with search is an example of RAG, which is a pattern this article is promoting: better results from the LLM because ChatGPT injected additional search result context into the model to accompany your question.
- SpaghettiX 2y agoI agree. The author seems only happy with RAG, insert nail/hammer quote. I think of contradictory examples where the article doesn't make sense: LLM Chat products are just the product of training data. Generative coding applications take 1 prompt and generate a lot of code. What this article did make me think is the existing Chat UI in coding apps are too limiting. Some have image attachments, but we need to allow users to input more detail in their prompt (visually, or by having a pre-generation discussion about specifics). That's why I think product engineers will benefit from AI more than non technical folk. Also, do you have resources that align with those opinions?
- slowmovintarget 2y agoThe real lesson should be, use an LLM when an educated guess is good enough. They're probabilistic systems doing best-fit math. If guessing is OK or even reasonable (in a feedback loop with an actual person, for example) then an LLM can be useful. If guessing is not OK, if you need algorithmic rigidity, just write code, don't introduce D&D dice rolls into your system. Example: Should we use "AI" for authentication and authorization? For logging in: No. For checking authority for an operation: No. For determining the likelihood that the IP address a login attempt is coming from is part of an attack pattern: Yes!