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83 points wavelander | 1 comments | | HN request time: 1.639s | source
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NeutralCrane ◴[] No.41214178[source]
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.

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1. isaacbmiller ◴[] No.41216615[source]
Disclaimer: original blog author

> as opposed to solving an actual problem

This was literally the point of the post. No one really knows what the future of LLMs will look like, so DSPy just iteratively changes in the best way it can for your metric (your problem).

> someone actually using for something other than a toy example

DSPy, among the problems I listed in the post, has some scalability problems, too, but I am not going to take away from that. There are at least early signs of enterprise adoption from posts like this blog: https://www.databricks.com/blog/optimizing-databricks-llm-pi...