ML//DSPy

- DSPy is a Stanford NLP framework for programming language-model pipelines declaratively and optimizing their prompts or examples against data and metrics.

DSPy is a Stanford NLP framework for programming language-model pipelines declaratively and optimizing their prompts or examples against data and metrics.

Define signatures (input→output), compose modules, optimize via few-shot examples or metrics.

The core move is separation of concerns: specify the inputs, outputs, modules, and objective, then let a compiler search for an effective prompting strategy.

This turns prompts from handwritten interface copy into generated parameters. It does not remove judgment; it moves judgment into the training set, metric, module boundaries, and optimizer budget.

prompt engineering : : DSPy replaces manual string tuning with evaluation-driven compilation