Replying to @⁨datavoid@sh.itjust.works⁩

Yeah I’ve been using LLM’s in Rider for over a decade. I don’t have a problem with LLM’s. I do have a problem with how they’re currently used, and how people keep trying to use them to replace their own thinking.

I think they have a place in the coding scene, a limited niche place, but a place none the less. They just aren’t a replacement for software engineers. Architecture and intention are the big differences to me. An LLM cannot understand intention, it just makes statistical guesses that are often wrong.

Replying to @⁨ViceroTempus@lemmy.world⁩

An LLM cannot understand intention, it just makes statistical guesses that are often wrong.

True, when you give a prompt like: “make me a contact management / constant contact app which I can deploy on AWS and scale to 100,000 users.” you get, mostly garbage. If you specify how you want the UX to flow, what fields are most important, what fields should be included in deeper interfaces, what the scheduling looks like, how it gets tuned, what the reports look like, etc. etc. etc. - in other words: give it real requirements and specifications.

Then, pay attention as it develops, you’ll ususally find that the requirements you gave it aren’t exactly what you really wanted, and when you see what it built that doesn’t match with your visions, you can have it revise the requirements and specs.

Replying to @⁨MangoCats@feddit.it⁩

Sure, but for me its just faster to write it myself, in a way that needs to fit into the project. And while I’m aware you can give this context to an LLM, it still can’t read what your previous intentions were, nor what they are currently. So its hard for it to build on it.

To me they’re best as auto-complete, or research tools in the same vein as StackOverflow. Something to help speed up your existing workflow. Also good at translating both spoken languages, and functions into other coding languages. Everything else seems to get in the way for me.

Replying to @⁨ViceroTempus@lemmy.world⁩

For professional work we hire experts (who use AI as their first pass, but then “clean it up” to their “high professional standards using native speakers”) - and then we do another pass with “subject matter experts” who both speak the language and work directly in our field, becaue the “professional humans” typically don’t have those niche experiences. We recently made this procedure 100% required after the “human professionals” effectively translated On to mean Off for one of our controls…

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