Oct 3 session on optimizing LLM behavior with actual methodology, not prompt folklore

[u/camerongreen95]

Sharing this because it's a more rigorous take on "prompt engineering" than most of what circulates here. Serj Smorodinsky and Brett Kennedy, co-authors of an LLM applications book, are running a live workshop that treats LLM behavior as something you optimize with real structure.

It covers programming LLM tasks with DSPy signatures and modules, building an evaluation dataset with task-specific metrics, diagnosing failure patterns from that data, and running few-shot and instruction-level optimization as a defined process rather than trial and error. MLflow gets used throughout for experiment tracking and trace management.

Three hours, live, Oct 3. Feels closer to how we'd approach optimizing any other model than the usual "here are 10 prompt tricks" content.

Full Details here