[danlenton]

One use case is optimizing agentic systems, where a custom router [https://youtu.be/9JYqNbIEac0] is trained end-to-end on the final task (rather than GPT4-as-a-judge). Both the intermediate prompts and the models used can then be learned from data (similar to DSPy), whilst ensuring the final task performance remains high. This is not supported with v0, but it's on the roadmap. Thoughts?

[qeternity]

We do agentic systems. We already optimize for these things. We route between different models based on various heuristics. I absolutely would not want that to be black box. And doing any sort of vector similarity to determine task complexity is not going to work well.

I would also not try to emulate DSPy, which is a massively overrated bit of kit and of little use in a production pipeline.

[tarasglek]

Curious, can you explain why you think DSPy overrated?