🚀 DSPy Code Practice

Free Hands-on Labs for Declarative LLM Programming & AI Engineering

🎯 Educational Mission

A comprehensive, vendor-independent DSPy learning environment designed for developers, data engineers, data scientists, and AI practitioners who want to master declarative LLM programming through hands-on practice. Built for the community, completely free and open source.

11

Progressive Labs

80+

Hands-on Exercises

4

Learning Phases

100%

Free & Open Source

🎓 Progressive Learning Path

Phase 1: Foundation

Labs 0-2

  • Environment Setup & Configuration
  • DSPy Fundamentals & Paradigm
  • First Complete Programs

Phase 2: Core Concepts

Labs 3-5

  • Signatures & Modules
  • Data & Evaluation
  • Optimization Strategies

Phase 3: Advanced Patterns

Labs 6-8

  • Chain of Thought
  • Multi-Stage Programs
  • RAG Systems

Phase 4: Production

Labs 9-11

  • AI Agents
  • Real-World Applications
  • Production Deployment

📚 Comprehensive Lab Curriculum

Lab 0: Environment Setup

Install DSPy, configure LLM providers, and validate your development environment with hands-on exercises.

Lab 1: DSPy Fundamentals

Understand the declarative programming paradigm, learn core concepts: Signatures, Modules, and Teleprompters.

Lab 2: Your First DSPy Program

Build complete DSPy applications end-to-end with error handling and practical exercises.

Lab 3: Signatures and Modules

Master DSPy's core building blocks, design effective signatures, and compose complex modules.

Lab 4: Data and Evaluation

Learn to create quality datasets, implement evaluation metrics, and measure program performance.

Lab 5: Optimization Strategies

Make DSPy programs self-improving using teleprompters and advanced optimization techniques.

Lab 6: Chain of Thought

Implement complex reasoning patterns, self-consistency, and tree-of-thoughts methods.

Lab 7: Multi-Stage Programs

Build sophisticated AI pipelines with conditional routing, parallel processing, and feedback loops.

Lab 8: Retrieval Augmented Generation

Create RAG systems with vector retrieval, query expansion, and advanced ranking strategies.

Lab 9: Building AI Agents

Create autonomous AI systems with tool use, memory management, and multi-agent collaboration.

Lab 10: Real-World Applications

Build practical applications: customer support, document analysis, recommendation systems.

Lab 11: Production Deployment

Deploy DSPy applications with Docker, FastAPI, monitoring, scaling, and security best practices.

🚀 Quick Start Guide

Begin your DSPy learning journey in minutes:

  1. Clone the repository: git clone https://github.com/nellaivijay/dspy-code-practice.git
  2. Install dependencies: pip install -r requirements.txt
  3. Set up LLM provider: Configure your OpenAI, Anthropic, or local model API key
  4. Start learning: Begin with Lab 0: Environment Setup

🎓 Educational Features

Our learning environment includes:

  • Interactive Notebooks: Jupyter notebooks for hands-on practice
  • Progressive Complexity: Labs build skills step-by-step
  • Real-World Projects: Practical AI engineering applications
  • Production Patterns: Best practices for deployment and operations
  • Vendor Independence: Works with multiple LLM providers
  • Community Driven: Open source with community contributions

🔗 Learning Resources

Official DSPy Documentation Comprehensive Wiki GitHub Repository