Dspy

Quick Start

# Install DSPy
pip install dspy-ai

# Optional: For retrieval
pip install chromadb faiss-cpu

# Set API key
export OPENAI_API_KEY="your-api-key"

When to Use This Skill

USE when:

• Need to optimize prompts programmatically rather than manually
• Building pipelines where prompt quality is critical to success
• Want reproducible, testable prompt engineering
• Working with complex multi-step reasoning tasks
• Need to automatically find effective few-shot examples
• Building systems that improve with more training data
• Require systematic evaluation and comparison of prompt strategies
• Want to abstract away prompt engineering from application logic

DON'T USE when:

• Simple single-shot prompts that work well as-is
• Need fine-grained control over exact prompt wording
• Building applications with minimal LLM interactions
• Prototyping where rapid iteration is more important than optimization
• Resource-constrained environments (optimization requires API calls)

Prerequisites

# Core installation
pip install dspy-ai>=2.4.0

# For vector retrieval
pip install chromadb>=0.4.0 faiss-cpu>=1.7.0

# For different LLM providers
pip install openai>=1.0.0 anthropic>=0.5.0

# For evaluation
pip install pandas>=2.0.0 scikit-learn>=1.0.0

# Environment setup
export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."

Resources

• DSPy Documentation: https://dspy-docs.vercel.app/
• DSPy GitHub: https://github.com/stanfordnlp/dspy
• DSPy Paper: https://arxiv.org/abs/2310.03714
• Examples: https://github.com/stanfordnlp/dspy/tree/main/examples

Version History

• 1.0.0 (2026-01-17): Initial release with signatures, modules, optimizers, and RAG

Sub-Skills

• 1. Signatures
• 2. Modules
• 3. Retrieval-Augmented Generation
• 4. Optimizers
• 5. Evaluation and Metrics (+1)
• 1. Start Simple, Then Optimize (+2)
• Optimization Not Improving (+2)

Sub-Skills

• DSPy Philosophy
• Example 1: Engineering Report Analysis Pipeline (+2)
• Integration with LangChain (+1)