Contrarily, it didn't seem to care a lot about roleplays other than for occasional compliments for reinforcements. If anything, old HN style blunt interactions seemed to help.
Same pattern happens with LLMs, in my experience. GP says an LLM infrerence is "sort of a decompression process for a lossy copy of the Internet" - but in these terms, if asking it for a cheeseburger means decompressing parts of the latent space around the term "cheeseburger", then asking for "a hamburger with patty topped with sliced cheese and standard condiments" is making it decompress much larger space around multiple terms, and then filter the result out into a semantically relevant subspace, and then run extra inference on that.
If you think about it, the very reason we (humans) give names to things is to avoid having to repeatedly do that decompression and filtering every time we want to refer to a specific thing. We call the modified "hamburger" a "cheeseburger" precisely to avoid having to talk like GP suggests we should talk to LLMs, so I very much think this advice is backwards.
With an intern, I give them some search terms and let them go learn. I don't have to do the searching for them. It's actually more important to help them learn how to evaluate the different results. It's not even that they "don't want to admit they don't know" (which is anthropomorphization), they are not designed or trained to ask clarifications. The chat based interaction is an "afterthought" (a round of fine tuning after initial training)
The big issues I see in this paradigm are
(1) you have to know a lot of things already to do this
(2) if we automate all the low hanging fruit, how will we develop humans to the level of understanding to do this
(3) with a human, I can delegate, with an AI, I have to handhold. As much as people want to call it "pair programming" it is often more like having to teach except in never truly learns, so I never get my lost time back
I disagree with (3) based on my experience. That feeling happens when I am not providing enough context. I rarely have experiences where I step back to provide more context and still end up in a dumb loop. Highly recommend providing lots of context + breaking down the problem more.
I would love to dig deeper into an example you have where you feel you "never get your time back". Because in general I am saving a lot of time from how much less typing I have to do.
> Highly recommend providing lots of context + breaking down the problem more.
I don't have to do this with a human, I can delegate and know they will on their own. Also, the AI will make the same mistake if I come back a week later, a human learns online. This is at the core of lost time and I don't think AIs are close to this level without an advancement that takes us beyond transformers
The fundamental problem is the tools being marketed and onboarded that way. I don't blame them for getting frustrated.
You can't expect everyone to simply understand the limitations of LLMs and how the tool might be implemented. There is a user discovery issue at play here.
