Regression Testing for Reliable Generative AI Applications

Generative AI applications are notoriously unpredictable, making it difficult to ensure that updates to prompts or models do not break existing functionality. Regression testing provides the structured framework needed to measure, evaluate, and maintain the quality of your AI outputs over time. By establishing systematic evaluation pipelines, you can confidently deploy updates without worrying about silent failures or degraded performance. In this course, you will transition from manual, ad-hoc testing of language model outputs to building automated regression testing workflows. You will discover how to systematically detect regressions, evaluate response quality, and maintain high standards of reliability for your AI-driven applications through structured, written exercises and code analyses. What you'll learn: - Understand the core principles of regression testing specifically tailored for generative AI and language model outputs. - Build representative evaluation datasets to test your application against diverse real-world scenarios. - Apply modern scoring metrics, including semantic similarity, toxicity detection, and hallucination evaluation. - Implement the "LLM-as-a-judge" evaluation pattern to automate complex quality assessments. - Integrate testing frameworks into automated CI/CD pipelines for continuous quality assurance. - Analyze test results to safely iterate on prompts and model parameters without breaking existing features. This course begins with essential terminology, basic concepts, and foundational definitions of generative AI evaluation. You will then progress through detailed written explanations and practical code snippets that demonstrate how to construct test suites, apply programmatic metrics, and automate the entire evaluation lifecycle. This course is designed for software developers, QA engineers, and technology professionals who want to bring engineering discipline to generative AI. No prior experience with AI testing or advanced machine learning is required. Read this guide to establish a reliable, automated testing pipeline for your generative AI projects.