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I think it is a tooling issue. It is in no way obvious how use LLM's effectively, especially for really good writing results. Tweaking and tinkering can be time consuming indeed, but i use lately the chatgpt-shell [1] and it lends well to an iterative approach. One needs to cycle through some styles first, and then decide how to most effectively prompt for better results.
[1]https://github.com/xenodium/chatgpt-shell/blob/bf2d12ed2ed60...
Good question.
I expect that we're moving into a phase of AIs talking to AIs, and initially it'll be wasteful (because it'll be mostly English), but eventually, they'll derive their own language and seamlessly upgrade protocols when they determine they're talking to an AI. No clue how that will come about or what that language will look like, but honestly, it's kind of exciting.
Really interesting to think about how they might handle context, as well. Even though we have much bigger context windows (and they'll only get larger), context management is still a resource-management issue, which we'll probably continue to refine, as well. Imagine different strategies for managing both what is brought into the context of each request, as well as what form it could take (level of detail, additional references or commentary on it, etc). Things could get really unreadable even in English, and still be very interpretable for an LLM.
W.r.t. the graph-oriented interfaces, are you thinking something like Node-RED [1]? I'm seeing more and more people mention having LLMs produce non-text or structured outputs, like JSON, UI, and other things. Easy to imagine an LLM that wires together various open-source platforms, on-demand. Something like Node-RED for pipelines/functions, some UI tools for visualization/interactivity, other platforms for messaging, etc...
[1] https://nodered.org/
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