These libraries mostly exist as "cope" for the fact that we don't have good fine-tuning (i.e. lora) capabilities for ChatGPT et al, so we try to instead optimize the prompt.
> nothing more than fancy prompt chains under the hood
Some approaches using steering vectors, clever ways of fine-tuning, transfer decoding, some tree search sampling-esque approaches, and others all seem very promising.
DSPy is, yes, ultimately a fancy prompt chain. Even once we integrate some of the other approaches, I don't think it becomes a single-lever problem where we can only change one thing(e.g., fine-tune a model) and that solves all of our problems.
It will likely always be a combination of the few most powerful levers to pull.
https://github.com/stanfordnlp/pyreft
Anything Christopher Manning touches turns to gold.