LLM Prompt Engineering: Evaluation and Documentation

As large language models become central to modern software, writing a good prompt is only the first step. To build reliable applications, you must be able to systematically measure, document, and optimize how your prompts perform under different conditions. This text-based course guides you from the absolute basics of prompt engineering to establishing robust evaluation workflows. You will learn how to define clear performance metrics, document version changes, and run systematic tests to ensure your AI outputs remain accurate, safe, and consistent. What you'll learn: Understand foundational prompt engineering concepts and the necessity of systematic evaluation; Design structured documentation templates to track prompt versions, system instructions, and model variables; Establish key performance indicators for evaluating LLM outputs, including accuracy, relevance, and safety; Apply modern evaluation methodologies, such as LLM-as-a-judge patterns and basic automated scoring; Analyze test results to identify prompt drift, hallucination risks, and areas for refinement; Implement version control practices for prompts to maintain consistency across software updates. You will start by exploring core terminology and documentation standards before moving on to practical evaluation frameworks and testing methodologies. Through clear written explanations and structured examples, you will gain a repeatable workflow for prompt optimization. This course is designed for beginners, developers, and product managers looking to bring discipline to their AI workflows. No prior machine learning or programming experience is required. Start building more reliable AI systems by mastering the art of prompt evaluation today.