Interesting counterpoint to "just scale up": a workshop on optimizing what you already have (DSPy, Oct 3)

Most of what gets discussed here is scaling compute and data. This one's the opposite end of the lever, given a fixed model, how much performance are you leaving on the table because your prompting and evaluation setup is unprincipled.

Serj Smorodinsky and Brett Kennedy (co-authors of a book on LLM applications) are running a live session on Oct 3 built around DSPy: defining tasks as signatures/modules, building a real eval set with task-specific metrics, then running few-shot and instruction-level optimization as a formal search rather than manual guessing. MLflow tracks the whole process for reproducibility.

Not a scaling talk, more a "how much of your perceived model quality is actually a measurement and optimization problem" talk. Sharing since it felt relevant to the broader question of where gains actually come from.

Link for full details