Real-World Applications

Introduction

In the previous chapters, we mastered the core building blocks of DSPy: Signatures, Modules, Metrics, and Optimizers. Now, we apply these tools to solve complex, real-world problems.

This chapter focuses on the most common and high-value applications of Large Language Models, starting with Retrieval-Augmented Generation (RAG) and moving into autonomous agents, precise classification tasks, and specialized research workflows.

Learning Objectives

• Build Robust RAG Systems: Create pipelines that can search, filter, and synthesize information from large document bases.
• Master Multi-Hop Reasoning: Design systems that can answer complex questions requiring information from multiple disparate sources.
• Develop Intelligent Agents: Create agents that can use tools and plan dynamic sequences of actions.
• Implement Complex Classification: Handle difficult categorization tasks with high precision using DSPy optimizers.
• Explore Advanced Architectures: Learn about Multi-Agent RAG, GraphRAG, and Perspective-Driven Research.

Chapter Roadmap