Whatever you believe about what the Right Thing should be, you can’t control it by refusing what is happening right now. Skipping AI is not going to help you or your career. Think about it. Test these new tools, with care, with weeks of work, not in a five minutes test where you can just reinforce your own beliefs. Find a way to multiply yourself, and if it does not work for you, try again every few months.
P.S. I learned that not all of you are developers.
Those paragraphs are what I’m talking about. The author fails to explain what the LLM actually did that was helpful. It’s like saying “I used ChatGPT to plan my wedding menu” without any more details. What did it actually do? Why was it helpful? Those are the things I continue to not understand about these tools.
I’m confused why you are confused.
I feel like it is pretty clear the author said “hey AI, do this thing.” The AI made an attempt, the author clarified a few things and maybe made some edits, and then was satisfied with the result.
Like your example of planning a wedding menu. I’m not sure where the ambiguity is. If someone said “I used chatgpt to plan my wedding menu”, I assume they prompted it something like “plan my wedding menu. I want something classy but cheap. No fish.” Then chatgpt spat out a few options, they provided feedback - “I dont like broccoli either” - and then they picked an option they like.
It seems we agree on the facts, but not on what “useful” or “helpful” means. I honestly have never, ever considered deciding on what food to serve guests be “labor”, but in the interests of replying in good faith I asked an LLM the exact prompt you gave. It gave a long, detailed reply, but here is the first part labeled “1. Welcome / Cocktail Reception”:
I want you to consider that this not actually helpful in the slightest, and is fact creating more work. Consider: is there a vendor nearby that has these items as an an option for event planning? Is this a recipe that even exists? Does this information further my mission of having a wedding in any conceivable way?
Or Perhaps:
they mention reinforcement learning, pre-training and other general LLM concepts, but none of these are related back to the tasks they are talking about.
The point is, there was no explanation of how any of this was achieved, which can lead to confusion about what was actually achieved.
The LLM wrote some docs vs the LLM rewrote the library from end to end are very different things.
It’s very much a “Don’t give up on X, look at what can be achieved” but without any actual details on what is required to achieve those results.