D
DSPy
Free
Programmatic framework for optimizing LLM prompts and weights
Stanford NLP · Frameworks
About DSPy
DSPy by Stanford NLP replaces brittle manual prompting with a programming model. Developers define modules and metrics; DSPy compiles and optimizes prompts automatically using techniques like few-shot learning and fine-tuning.
Key Use Cases
- Prompt optimization
- Pipeline compilation
- Research
- Systematic LLM development
Pros
- Automatic optimization
- Eliminates manual prompting
- Research-backed
Cons
- Steep learning curve
- Less tooling ecosystem
Alternatives to Consider
Details
Tags
FrameworkPrompt OptimizationResearchOpen-SourceNLP