DSPy forces you to think about your problem in very simple terms.
DSPy forces you to think about your problem in very simple terms. #learnai #genai #training #shorts
It might be worthwhile to take a look at DSP at each stage, maybe DSP for prompting DSP Farag, DSP for agents? Did I improve at each step? Am I making a zigzag improvement towards human level performance whether I'm overfitting or not? Aside, am I doing better? Exact exactly right and I think we should look at DSPy.
It forces you to think about your problem. Very simple terms in terms of an input and an ideal output. I think that is a useful thing to try to be doing across your stack at all times. Ultimately, we're designing systems that do things, so we should always be thinking about what is the input to the system and then what is the thing it has done right.
And if we can't describe that, just step back, figure out how we can fit it into that box. So it's a useful way to force yourself into thinking optimally about these kinds of applications. DSPy might feel a little out of the box sometimes, but it forces you into the right box.