Testing and Evaluating AI Applications with the OpenAI API

Building AI applications is only the first step; ensuring they produce reliable, accurate, and safe outputs is the real challenge. This text-based course teaches you how to systematically test and evaluate your AI systems to prevent unexpected behavior and improve user trust. You will transition from guessing whether your prompts work to scientifically measuring their performance. You will learn how to design automated test suites, use the OpenAI API to evaluate outputs, and establish clear benchmarks for your AI models. What you will learn: 1. Understand the core principles of LLM evaluation and why traditional software testing falls short. 2. Configure the OpenAI API to run automated evaluation tests on your application outputs. 3. Implement LLM-as-a-judge patterns to programmatically grade prompt accuracy and relevance. 4. Measure key metrics including faithfulness, context recall, and semantic similarity. 5. Integrate evaluation tests into your development workflow using basic python testing patterns. 6. Optimize prompts systematically based on quantitative test data. The course begins with foundational concepts of AI testing and evaluation metrics before guiding you through hands-on code examples. You will read through practical testing scenarios, examine structured evaluation outputs, and learn how to run systematic tests on your own AI applications. This course is designed for software developers, product managers, and AI enthusiasts who want to bring engineering discipline to their AI projects. No advanced machine learning background is required—basic familiarity with Python and APIs is all you need. Start measuring and improving your AI application performance today.