Could we have a concise and specific explanation how DSPy works?
All I've seen are vague definitions of new terms (ex. signatures) and "trust me this very powerful and will optimize it all for you".
Also, what would a good way to reason between DSPy and TextGrad?
I had a few problems with DSPy:
* Multi-hop reasoning rarely works with real data in my case. * Impossible to define advanced metrics over the whole dataset. * No async support
Not to say anything about dspy, but I really liked the take on hvat we should use llms for.
We need to stop doing useless reasoning stuff, and find acttual fitting problems for the llms to solve.
Current llms are not your db manager(if they could be you don't have a db size in the real world). They are not a developer. We have people for that.
Llms prove to be decent creative tools, classificators, and qna answer generators.
I tried it recently and it is kinda fun: https://www.lycee.ai/courses/a5b7d115-c794-410d-92f2-15d8f29...
Whenever i see "ChainOfThought" for AI, it's an annoying and misleading term. Machine never never thinks at all.
The more I’ve looked at DSPy, the less impressed I am. The design of the project is very confusing with non-sensical, convoluted abstractions. And for all the discussion surrounding it, I’ve yet to see someone actually using for something other than a toy example. I’m not sure I’ve even seen someone prove it can do what it claims to in terms of prompt optimization.
It reminds me very much of Langchain in that it feels like a rushed, unnecessary set of abstractions that add more friction than actual benefit, and ultimately boils down to an attempt to stake a claim as a major framework in the still very young stages of LLMs, as opposed to solving an actual problem.