I suspect you're right for how people are using and deploying LLMs now: hacking all kinds of functionality out of a text-completion model that, although it enco

I suspect you're right for how people are using and deploying LLMs now: hacking all kinds of functionality out of a text-completion model that, although it encodes a ton of data and some reasoning, is fundamentally still a text completion model and when deployed via commercial APIs like today without fine tuning, are not flexible beyond prompt engineering, chaining, etc. make possible. But I think we've only scratched the surface as to what LLMs fine-tuned on specific tasks, especially for abstract reasoning over narrow domains, could do. These applications possibly won't look anything like the chat interfaces that people are getting excited about now, and fine-tuning is not as accessible as prompt engineering. But there's a whole lot more to explore.