I think the real problem with using DSPy is that many of the problems people are trying to solve with LLMs (agents, chat) don't have an obvious path to evaluate. You have to really think carefully on how to build up a training and evaluation dataset that you can throw to DSPy to get it to optimize.

This takes a ton of upfront work and careful thinking. As soon as you move the goalposts of what you're trying to achieve you also have to update the training and evaluation dataset to cover that new use case.

This can actually get in the way of moving fast. Often teams are not trying to optimize their prompts but even trying to figure out what the set of questions and right answers should be!