DSPy in Depth

Programming Language Models from Zero to Production

Introduction: The End of Prompt Engineering as We Know It

Chapter 1: What Is DSPy and Why It Matters

1. The Prompting Crisis
2. From Prompting to Programming
3. The DSPy Philosophy
4. How DSPy Fits in the Ecosystem

Chapter 2: Installation and Your First Program

1. Installing DSPy
2. Configuring Your Language Model
3. Your First Signature and Prediction
4. Understanding the Output
5. Switching Between Models
6. A Complete First Program
7. Migration Notes from Older Versions

Chapter 3: Signatures in Depth

1. String-Based Signatures
2. Class-Based Signatures
3. Input Fields and Output Fields
4. Type Annotations and Pydantic Integration
5. Signature Mutation and Composition
6. Signature Introspection
7. Designing Effective Signatures

Chapter 4: Core Modules

1. dspy.Predict: Basic Prediction
2. dspy.ChainOfThought: Step-by-Step Reasoning
3. dspy.ProgramOfThought: Code-Assisted Reasoning
4. dspy.MultiChainComparison: Comparing Outputs
5. dspy.BestOfN and dspy.Refine: Output Refinement
6. dspy.Parallel: Concurrent Execution
7. Choosing the Right Module

Chapter 5: Composing Custom Modules

1. The dspy.Module Base Class
2. Building Your First Compound Module
3. Forward Method Design Patterns
4. Nested Modules and Sub-modules
5. Module Inspection and Introspection
6. Deep Copy and State Management

Chapter 6: Language Models and Configuration

1. The dspy.LM Interface
2. OpenAI, Anthropic, and Google Providers
3. Local Models and OpenAI-Compatible Endpoints
4. Context Management with dspy.context()
5. Caching and Performance
6. Tracking Usage and Costs
7. Adapter Selection

Chapter 7: Evaluation and Metrics

1. Writing Metric Functions
2. The dspy.Evaluate Class
3. Built-in Metrics
4. LLM-as-a-Judge Evaluation
5. Debugging with inspect_history()
6. Composite Metrics
7. Evaluation Best Practices

Chapter 8: Optimizers (Teleprompters)

1. The Optimization Paradigm
2. BootstrapFewShot Family
3. COPRO: Instruction Optimization
4. MIPROv2: Joint Instruction and Demo Search
5. GEPA: Reflective Prompt Evolution
6. SIMBA and Other Specialized Optimizers
7. Choosing the Right Optimizer
8. Saving and Loading Compiled Programs

Chapter 9: Retrieval-Augmented Generation (RAG)

1. The dspy.Retrieve Module
2. ColBERTv2 Integration
3. Building a Basic RAG Pipeline
4. Multi-Hop RAG
5. Optimizing RAG Programs
6. Custom Retriever Integration

Chapter 10: Agents and Tool Use

1. The ReAct Paradigm
2. dspy.ReAct Module
3. Defining Tools as Functions
4. Building Production Agents
5. Advanced Tool Patterns

Chapter 11: Structured Outputs and Type Safety

1. Typed Predictors
2. Pydantic Models in Signatures
3. JSON Schema Generation
4. Output Validation Strategies
5. Complex Nested Structures

Chapter 12: Advanced Patterns and Workflows

1. Assertions and Constraints (Refine Module)
2. Multimodal Programs with Images and Audio
3. Conversation History Management
4. Async and Streaming Support
5. MCP Integration
6. Caching Strategies for Production

Chapter 13: Production Deployment

1. Saving and Loading Optimized Programs
2. FastAPI Deployment
3. MLflow Integration
4. Caching Strategies
5. Monitoring and Observability

Chapter 14: Performance Optimization and Best Practices

1. Cost Optimization
2. Latency Reduction
3. Error Handling Patterns
4. Testing DSPy Programs
5. Common Pitfalls and Anti-Patterns

Chapter 15: Real-World Projects

1. Project 1: Intelligent Customer Service Agent
2. Project 2: Multi-Document Research Assistant
3. Project 3: Automated Code Review System

Conclusion: The Future of LLM Programming

References