DSPy Foundations: Building and Optimizing AI Agents

Relying on manual prompt engineering makes language model applications fragile and difficult to maintain. To build truly reliable AI agents, you need a systematic, programmatic approach to optimization. This text-only course guides you through the foundational concepts of DSPy and MLflow, enabling you to build, debug, and optimize robust agentic workflows. By reading through our clear explanations and practical code examples, you will transform how you build language model applications. What you'll learn: - Understand the core concepts of DSPy, including signatures, modules, and teleprompters - Design agentic workflows that solve complex tasks through systematic, programmatic logic - Integrate Retrieval-Augmented Generation (RAG) patterns to connect your agents to external data sources - Optimize LLM prompts and weights automatically using DSPy compilers and assertions - Track and evaluate your agent performance systematically using MLflow The course starts with basic terminology and foundational definitions of declarative programming for language models. You will then progress through structured written lessons that show you how to compile, evaluate, and refine multi-step agentic systems. This course is designed for software developers and AI enthusiasts who are new to DSPy; basic familiarity with Python is recommended. Begin reading now to build more predictable and powerful AI agents.