Foundations of LLM Application Testing and Evaluation

As Large Language Models (LLMs) become central to modern software, ensuring their reliability, accuracy, and safety is more critical than ever. Building an AI application is only the first step; knowing how to systematically test and evaluate its outputs is what makes it production-ready. This text-based course will guide you through the core principles of LLM quality assurance. You will start with foundational AI terminology and gradually explore how to measure model performance, structure evaluation datasets, and implement regression tests. By reading through practical scenarios and written code snippets, you will discover how to transition from manual prompt-checking to automated, scalable testing methodologies. What you will learn: Understand foundational LLM concepts, including the differences between fine-tuning and Retrieval-Augmented Generation (RAG). Design and curate robust evaluation datasets tailored to specific application use cases. Apply modern evaluation metrics to assess text generation quality, relevance, and factual accuracy. Implement regression testing to ensure model updates or prompt changes do not degrade existing features. Evaluate RAG architectures using contemporary patterns like LLM-as-a-judge and context-relevance scoring. Practice basic security testing concepts to identify and mitigate prompt injection vulnerabilities. The curriculum flows logically from basic definitions of AI evaluation to practical testing workflows. You will read through step-by-step written examples that demonstrate how to set up reliable testing pipelines for modern AI applications. This course is designed for beginners, QA professionals, and aspiring developers with basic programming knowledge who want to learn how to test AI applications. No prior machine learning expertise is required. Start reading today to build the skills necessary to confidently evaluate and test modern LLM applications.