Programming Generative AI with DSPy: A Practitioner's Guide to Building Self-Improving Language Model Applications
Audience: Software developers, ML engineers, and technical teams seeking a structured, maintainable approach to building LLM-powered applications. Also valuable for data scientists, MLOps engineers, technical leads, and advanced students who want to move beyond prompt engineering to robust, production-grade AI systems.
Description: This comprehensive, practitioner-focused guide empowers engineers and technical teams to build robust, production-ready generative AI applications using DSPy, Stanford's transformative framework for systematic AI programming. Moving beyond fragile prompt engineering, the book teaches readers how to design, optimize, and deploy self-improving LLM pipelines with DSPy’s declarative modules, advanced optimization algorithms, and enterprise-grade integration features. With detailed explanations, annotated Python code, and real-world business examples, readers will master maintainable, scalable, and trustworthy AI systems for modern organizations.
Table of Contents
Chapter 01: Beyond Prompt Hacking: Why DSPy Is the Modern Approach to AI Programming
Objective: Introduce the limitations of traditional prompt engineering, establish the need for DSPy's systematic and maintainable approach, and provide an overview of the book's learning journey.
Sections:
- Section 1.1: Recognizing the Limits of Prompt Engineering
- Topics
- Common business pain points with prompt-based systems
- How prompt tweaks lead to brittle solutions
- Real-world failures and maintenance challenges
- Section 1.2: Embracing Code-Driven AI: The DSPy Paradigm
- Topics
- What makes DSPy different from prompt engineering
- Benefits of modular, code-driven AI logic
- Case studies: Enterprises adopting DSPy
- Section 1.3: Your Learning Journey
- Topics
- Book structure and chapter prerequisites
- Skills and outcomes readers can expect
- Overview of hands-on approach and business relevance
Chapter 02: Preparing Your Environment: Installing DSPy and Ensuring Compatibility
Objective: Guide readers through installing DSPy, configuring LLM providers, and preparing a reproducible, future-proof environment for hands-on experimentation.
Sections:
- Section 2.1: Installing DSPy and Managing Dependencies
- Topics
- Supported Python versions and package managers
- Installing DSPy via pip, Poetry, or Conda
- Managing virtual environments and reproducibility
- Using pyproject.toml, lockfiles, and dependency pinning
- Ensuring compatibility with latest DSPy, Python, and third-party libraries
- Section 2.2: Connecting to Modern Language Models
- Topics
- Configuring OpenAI, Anthropic, and local models (Ollama, SGLang)
- API keys, authentication, and environment variables
- Practical tips for switching between providers
- Monitoring for API version changes and deprecations
- Section 2.3: Choosing Development and Collaboration Tools
- Topics
- Recommended editors (VS Code) and extensions
- Working with notebooks vs. scripts
- Using GitHub and CI/CD for AI pipelines
- Hardware considerations: RAM, GPUs, and cost management
- Section 2.4: Running and Troubleshooting Your First DSPy Program
- Topics
- Hello World with DSPy
- Understanding output and common setup issues
- Practical exercise: Verifying your environment
Chapter 03: DSPy Fundamentals: Modules, Signatures, and Declarative AI Design
Objective: Introduce DSPy's core concepts—modules, signatures, and declarative programming—and demonstrate how they enable reusable, maintainable AI components.
Sections: