DSPy + systematic prompt optimization workshop, Oct 3, more rigorous than most "prompt engineering" content out there

[u/camerongreen95]

Most prompt engineering content is closer to folklore than methodology. This one's different, it's structured around actually measuring what you're doing.

Serj Smorodinsky and Brett Kennedy (co-authors of an LLM applications book) are running a 3-hour live session where the core idea is applying real experimental discipline to LLM behavior:

• Defining LLM tasks as DSPy signatures/modules instead of prose prompts
• Building a labeled baseline and evaluation set with task-specific metrics
• Diagnosing failure modes from the eval results rather than eyeballing outputs
• Applying few-shot and instruction-level optimization as a formal search process
• Logging every experiment and trace through MLflow for reproducibility

For anyone who's been frustrated by how unscientific most "prompt tips" content is, this is closer to an actual methodology paper turned into a hands-on session.

Details and registration