DSPy Course
Chapter 1: DSPy Fundamentals
Learning Objectives:
- Understand DSPy’s core philosophy and design principles
- Master the differences between DSPy and traditional prompt engineering
- Learn DSPy’s architectural components and workflow
- Configure DSPy development environment
- Run your first DSPy program
Brief Description: This chapter introduces the basic concepts of the DSPy framework and teaches how to transition from traditional prompt engineering to programmatic language model programming.
Chapter 2: DSPy Core Concepts and Components
Learning Objectives:
- Master DSPy’s Signature mechanism
- Understand the concept and usage of Modules
- Learn how Predictors work
- Understand data types and structures in DSPy
- Master basic input and output processing
Brief Description: Deep dive into DSPy’s core abstractions, understanding how to build language model programs using signatures, modules, and predictors.
Chapter 3: DSPy Predictors in Detail
Learning Objectives:
- Learn Chain of Thought (CoT) predictors
- Master the use of Retrieve predictors
- Understand ChainOfThoughtWithHint predictors
- Explore ReAct predictor applications
- Methods for creating custom predictors
Brief Description: Comprehensive study of various predictor modules provided by DSPy, mastering the applicable scenarios and configuration methods for different predictors.
Chapter 4: DSPy Optimizers and Compilation
Learning Objectives:
- Understand how the DSPy compilation process works
- Learn Bootstrap Few-Shot optimizer
- Master the use of LabeledFewShot optimizer
- Explore COPRO optimizer functionality
- Understand optimizer evaluation and tuning strategies
Brief Description: Learn DSPy’s automatic optimization mechanism and how to use different optimizers to improve program performance.
Chapter 5: Retrieval Augmented Generation (RAG) with DSPy
Learning Objectives:
- Implement retrieval augmented generation in DSPy
- Configure and use vector databases
- Design retrieval strategies and evaluation metrics
- Build multi-hop retrieval systems
- Optimize the synergy between retrieval and generation
Brief Description: Learn how to build efficient retrieval augmented generation systems in the DSPy framework to handle knowledge-intensive tasks.
Chapter 6: DSPy Data Processing and Evaluation
Learning Objectives:
- Master dataset processing methods in DSPy
- Learn to define and use evaluation metrics
- Implement custom evaluation functions
- Conduct model performance comparative analysis
- Design A/B testing frameworks
Brief Description: Learn DSPy’s data processing pipeline and how to design appropriate evaluation systems to measure program performance.
Chapter 7: Multi-Step Reasoning and Complex Task Decomposition
Learning Objectives:
- Design multi-step reasoning DSPy programs
- Implement task decomposition and subtask coordination
- Build conditional execution and branching logic
- Learn error handling and exception recovery
- Optimize the performance of complex reasoning chains
Brief Description: Learn how to use DSPy to build complex multi-step reasoning systems that handle tasks requiring decomposition and coordination.
Chapter 8: DSPy and Language Model Integration
Learning Objectives:
- Configure different language model backends
- Learn integration with OpenAI, Claude, and local models
- Implement multi-model collaboration and switching
- Master model configuration and parameter tuning
- Understand model selection strategies
Brief Description: Learn how to integrate and manage different language models in DSPy, achieving flexible model selection and invocation.
Chapter 9: DSPy Advanced Patterns and Techniques
Learning Objectives:
- Implement program synthesis and automatic programming
- Learn meta-learning and few-shot learning techniques
- Master advanced prompt optimization strategies
- Explore self-consistency and voting mechanisms
- Implement dynamic prompt generation
Brief Description: Explore advanced usage patterns of DSPy, learning the application of cutting-edge technologies such as program synthesis and meta-learning.
Chapter 10: DSPy Production Deployment
Learning Objectives:
- Design architectural patterns for DSPy applications
- Implement caching and performance optimization
- Configure monitoring and logging systems
- Handle concurrency and scalability issues
- Deployment and maintenance best practices
Brief Description: Learn how to deploy DSPy programs to production environments, mastering key technologies for performance optimization, monitoring, and maintenance.
Chapter 11: DSPy Case Studies and Application Scenarios
Learning Objectives:
- Analyze DSPy implementations of question-answering systems
- Build text summarization and content generation systems
- Implement code generation and program repair
- Design dialogue systems and chatbots
- Explore approaches to multi-modal task processing
Brief Description: Learn DSPy’s practical applications in different domains through specific use cases, mastering problem analysis and solution design.
Chapter 12: Hands-On Project: Intelligent Knowledge Q&A System
Learning Objectives:
- Design end-to-end knowledge Q&A architecture
- Implement multi-source data retrieval and fusion
- Build complex reasoning and fact verification mechanisms
- Optimize system performance and user experience
- Deploy a complete production-grade application
Brief Description: Comprehensively apply various DSPy technologies and best practices by building a complete intelligent knowledge Q&A system.