What it does
A framework for programming and optimizing language-model pipelines rather than hand-tuning one prompt at a time.
Why it belongs here
Optimization beats prompt superstition: measure the task, tune the pipeline, and spend tokens where they actually move quality.
Best use case
Teams building language-model pipelines that need systematic optimization rather than manual prompt tweaking.
How to use it
Define the task, examples, and metrics, then let DSPy optimize pipeline components while tracking cost and quality tradeoffs.
Limits
It requires clear task metrics and data. Without that, optimization has little signal to work with.
Tags
optimizationprogrammingevals
