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DSPy

Framework for programming and optimizing language model pipelines with declarative modules and metrics.

Resource overview Use when prompt behavior should be optimized against metrics instead of manually tuned through ad hoc prompt edits.

Why use this

What it helps you do

Use this to move AI work from manual judgment to repeatable testing, tracing, metrics, prompt experiments, and quality improvement loops.

Who can use this

AI engineers, QA teams, platform teams, and product teams responsible for reliability, regression testing, and measurable AI quality.

Prerequisites

Representative test cases, logs or traces, target quality metrics, and enough production context to know what good and bad outputs look like.

System requirements

Python environment for most workflows, access to model calls or traces, datasets for evaluation, and storage for experiment results when running repeatedly.

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