Implementing the Evaluator-Optimizer Pattern for Iterative AI

Getting high-quality, reliable outputs from Large Language Models often requires more than a single prompt. By implementing the evaluator-optimizer design pattern, you can build systems that critique, refine, and polish their own work until it meets your exact standards. In this course, you will learn how to design separate evaluator and optimizer components, establish clear quality criteria, and manage iterative feedback loops to dramatically improve AI output quality. Through clear written explanations and practical code examples, you will master the mechanics of agentic workflows. What you'll learn: - Understand the core architecture of the evaluator-optimizer pattern and agentic design principles. - Design robust evaluator prompts that provide structured, actionable feedback. - Configure optimizer loops that consume feedback to iteratively correct and improve outputs. - Apply structured output formats to ensure reliable communication between AI components. - Implement termination conditions to prevent infinite loops and manage API costs. - Practice debugging and testing iterative AI workflows using written code walkthroughs. You will start by exploring the foundational theory of feedback loops before analyzing step-by-step code implementations of the pattern. Through practical written exercises, you will learn how to orchestrate these components into a seamless, self-correcting system. This course is designed for software developers, prompt engineers, and AI enthusiasts who want to move beyond basic prompting. A basic understanding of Python is helpful, but no prior experience with agentic frameworks is required. Start building self-refining AI systems today.