Earlier quoted context omitted.
"Specifically, the DSPy compiler will internally trace your program and then craft high-quality prompts for large LMs" I'm having trouble understanding the value provided here. A prompt is a string. Why abstract that string away from me like this? My instinct is that this will make it harder, not easier, for me to understand what's going on and make necessary changes.
"A neural network layer is just a matrix. Why abstract that matrix and learn it?" Well, because it's not your job to figure out how to hardcode delicate string or floats that work well for a given architecture & backend. We want developers to iterate quickly on system designs: How should we break down the task? Where do we call LMs? What should they do? --- If you can guess the right prompts right away for each LLM,…
1] when prototyping, it's useful to not have to tweak each prompt by hand as long as you can inspect them easily
2] when the system design is "final", it's important to be able to tweak any prompts or finetunes with full flexibility
But we may or may not agree on:
3] automatic optimization can basically make #2 above only very rarely needed
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Anyway, the entire DSPy project has zero hard-coded prompts for tasks. It's all bootstrapped and validated for your logic. In case you're worried that we're doing some opinionated prompting on your behalf.