האפקטיביות הבלתי מושכלת של הפרדת המשימה מהמודל

תקציר מקורי באנגליתBy declaring a task's inputs and outputs without initially considering model capability, you create the space needed to determine execution later. DSPy's promise is that AI engineering should happen above a particular prompt template or provider API shape: the Signature. That remains useful in a world of tools, RLMs, and Skills. Define a task strictly through its inputs and outputs, and the underlying implementation becomes flexible: experiment with models, settings, weights, templates, and output formats without touching the workflow. The talk covers DSPy 3.5 and previews DSPy 4.0, where models can write code beneath a signature and programs can learn directly from interactions with users while still respecting the signature's inputs and outputs. Speakers: Maxime Rivest — Core Contributor