Systematic Prompt Engineering: Iteration and Evaluation

Writing a single prompt rarely gives you the perfect AI response on the first try. To build reliable AI-powered workflows and applications, you need a structured approach to test, measure, and improve your instructions. This text-based course guides you through the entire lifecycle of prompt development, helping you progress from guessing what works to systematically evaluating prompt performance using reproducible methods. What you'll learn: - Understand foundational prompt engineering terminology and how language models interpret instructions. - Design structured prompt templates that allow for easy testing and variables. - Establish evaluation metrics to measure response quality, relevance, and accuracy. - Implement the LLM-as-a-judge pattern to automate prompt evaluation at scale. - Iterate on prompt versions systematically to continuously improve model outputs. - Apply version control concepts to your prompt library to track changes over time. You will start with core definitions and basic prompt structures before moving into systematic testing workflows. Through clear written explanations and realistic examples, you will learn how to design, test, and refine prompts step-by-step. This course is designed for beginners who want to move beyond basic chatting and start building reliable AI workflows, with no programming experience required. Read through the material and start optimizing your AI interactions today.