Comment by layer8
1 year ago
> Treat it as a naive but intelligent intern. Provide it data, give it a task, and let it surprise you with its output.
Well, I am a naive but intelligent intern (well, senior developer). So in this framing, the LLM can’t do more than I can already do by myself, and thus far it’s very hit or miss if I actually save time, having to provide all the context and requirements, and having to double-check the results.
With interns, this at least improves over time, as they become more knowledgeable, more familiar with the context, and become more autonomous and dependable.
Language-related tasks are indeed the most practical. I often use it to brainstorm how to name things.
I've recently started using an LLM to choose the best release of shows using data scraped from several trackers. I give it hard requirements and flexible preferences. It's not that I couldn't do this, it's that I don't want to do this on the scale of multiple thousand shows. The "magic" here is that releases don't all follow the same naming conventions, they're an unstructured dump of details. The LLM is simultaneously extracting the important details, and flexibly deciding the closest match to my request. The prompt is maybe two paragraphs and took me an hour to hone.
Ooh yeah it's great for bouncing ideas on what to name things off of. You can give it something's function and a backstory and it'll come up with a list of somethings for you to pick and choose from.