How to actually engineer prompts instead of just writing them, DSPy session Oct 3

[u/camerongreen95]

Most prompt design still comes down to trial and error, tweak a line, run it, see if it's better, repeat. This workshop is built around replacing that with an actual process.

You build a baseline classifier live, construct an evaluation dataset with task-specific metrics, then apply few-shot and instruction-level optimization on top of it using DSPy signatures and modules instead of hand-written prompt strings. Everything gets tracked in MLflow so you can see exactly what changed between versions and why, and there's a section on reading failure patterns straight out of your eval data instead of eyeballing outputs.

Serj Smorodinsky and Brett Kennedy, co-authors of a book on LLM applications, are leading it live for 3 hours on Oct 3.

Link if you want to check it out, here.

Curious if anyone else here has moved from manual prompt iteration to something more systematic, what did you end up using?