I use DSPy often, and it’s the only framework that I have much interest in using professionally. Evaluations are first class and have a natural place in optimization. I still usually spend some time adjusting initial prompts, but more time doing traditional ML things… like working with SMEs, building training sets, evaluating models and developing the pipeline. If you’re an ML engineer that’s frustrated by the “loose…
What do you actually use it for? I've never been able to actually get it to perform on anything remotely close to what it claims. Sure, it can help optimize few shot prompting...but what else can it reliably do?
Most important to me is that I can write evaluations based on feedback from the team and build them into the pipeline using suggestions and track them with LLM as a judge (and other) metrics. With some of the optimizers, you can use stronger models to help propose and test new instructions for your student model to follow, as well as optimize the N shot examples to use in the prompt (MIPROv2 optimizer).
It’s not that a lot of that can’t be done other ways, but as a framework it provides a non-trivial amount of value to me when I’m trying to keep track of requirements that grow over time instead of playing the whack a mole game in the prompt.