MusinglyAI
prompt-engineeringFast-moving

DSPy

Programming framework for LLM pipelines with automatic prompt optimization

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What it solves

The manual prompt tuning trap: replacing fragile hand-crafted prompts with a learnable pipeline that improves when shown examples.

Overview

DSPy reframes prompt engineering as a programming problem, you define a pipeline as a composition of modules with signatures (input/output specs), and an optimizer learns the best prompts and few-shot examples from your data. Shifts work from manual prompt tuning to systematic optimization.

Key facts

Language
Python
License
MIT
Maturity
Fast-moving
Maintainer
stanfordnlp
Reviewed
2026-06-11

Where to start

Define a signature, compile with an optimizer, see prompts improve. The quick-start reaches the core idea in ten minutes.

docs/docs/quick-start/installation.md

Technologies

dspypython