By 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, DSPy Maxime builds tools and content that make LLMs more accessible and powerful. He is a DSPy core contributor and an open-source Python library author. X/Twitter: https://x.com/MaximeRivest LinkedIn: https://linkedin.com/in/maximerivest Website: https://maximerivest.com Isaac Miller — Lead Maintainer of DSPy; Co-Founder, cmpnd Isaac leads DSPy and co-founded cmpnd, building an open-source framework for self-improving, modular AI systems. X/Twitter: https://x.com/isaacbmiller1 LinkedIn: https://www.linkedin.com/in/miller-isaac/