DSPy Documentation
by DSPy (Stanford NLP)
Program LLMs instead of prompting them — declare behavior, then let optimizers tune the prompts.
Overview
DSPy, from Stanford NLP, reframes LLM development: you write modular programs with typed signatures, and DSPy's optimizers automatically generate and tune the underlying prompts (and even few-shot examples) against a metric. The docs teach signatures, modules, and optimizers like MIPRO, showing how to build self-improving pipelines that are far more robust than hand-tuned prompt strings. A powerful, research-backed approach.
At a Glance
- Topic
- Frameworks
- Level
- Advanced
- Format
- Documentation
- Cost
- Free
- Duration
- Self-paced
- Provider
- DSPy (Stanford NLP)
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓Declaring LLM behavior with signatures and modules
- ✓Automatic prompt optimization with DSPy optimizers
- ✓Building robust, self-improving pipelines
- ✓Evaluating and compiling programs against metrics
Highlights
- •Programming paradigm over manual prompting
- •Research-backed prompt optimization
Who It’s For
Best For
- ✓Advanced developers tired of brittle prompt strings
Prerequisites
- •Solid Python
- •LLM app experience
FAQ
What is DSPy Documentation?
Docs for DSPy, a framework for programming (not prompting) LLMs, with modules and optimizers that compile and tune your pipelines.
Is DSPy Documentation free?
DSPy Documentation is free to access.
What level is DSPy Documentation for?
DSPy Documentation is aimed at a advanced audience. Recommended background: Solid Python, LLM app experience.
How long does DSPy Documentation take?
Expect roughly Self-paced. Most learners work through it at their own pace.
What will I learn from DSPy Documentation?
You'll learn: Declaring LLM behavior with signatures and modules; Automatic prompt optimization with DSPy optimizers; Building robust, self-improving pipelines; Evaluating and compiling programs against metrics.