Tag: dspy

This is the DSPy link I would hand to someone who keeps saying "prompt engineering" when they really mean eval-driven optimization. The example is concrete, costed, and honest about train, validation, and holdout splits.

Especially useful because it makes LLM application work feel closer to data science: define a task, build examples, pick a metric, optimize, and check whether the gains survive outside the optimizer.

Part one is the practical on-ramp: a transcript-search use case, a RAG-ish starting point, and the moment DSPy becomes more than a nicer prompt template.

Good to keep because it explains why declarative signatures and modules matter before jumping into optimizer results. The concept lands better when the first problem is messy enough to deserve it.