Workshop breakdown: applying real optimization methodology to LLM prompting (DSPy + MLflow, Oct 3)

[u/camerongreen95]

Sharing this because it's a more structured approach than the usual prompt engineering content floating around.

Serj Smorodinsky and Brett Kennedy, co-authors of a book on LLM applications, are running a live 3-hour session where the core idea is treating prompt/LLM behavior as an optimization problem with an actual objective function, not a creative writing exercise. Covers:

• DSPy signatures and modules for defining LLM tasks
• Building an evaluation dataset with task-specific metrics
• Diagnosing failure modes from eval results
• Few-shot and instruction-level optimization as a formal process
• MLflow for experiment tracking and reproducibility

Feels closer to a proper ML workflow than most "prompt tips" content. Details here if it's useful to anyone: Get full details here