Evaluating AI Performance and LLM Quality Metrics

Deploying artificial intelligence is only the first step; ensuring its outputs are accurate, safe, and consistent is where the real challenge begins. As generative models become core to modern software applications, learning how to systematically measure their performance is an essential skill for any developer or product owner. This course guides you through the fundamental methodologies for assessing LLM and AI system performance. You will transition from guessing whether your AI outputs are good enough to using structured, quantifiable metrics that guarantee reliability and safety in production environments. What you'll learn: - Understand core evaluation terminology, including precision, recall, and the unique challenges of generative AI outputs. - Apply automated evaluation metrics such as BLEU, ROUGE, and modern semantic similarity measures. - Implement the LLM-as-a-judge pattern to automate complex qualitative assessments. - Design human evaluation workflows and feedback loops to ground your automated testing. - Evaluate Retrieval-Augmented Generation (RAG) systems for faithfulness, answer relevance, and context recall. - Monitor AI applications in production to detect drift, bias, and performance degradation over time. You will start with foundational concepts of AI testing before exploring practical evaluation frameworks, code-based metric calculations, and continuous monitoring strategies. Through clear written explanations and step-by-step code walkthroughs, you will build a robust framework for AI quality assurance. This course is designed for software developers, product managers, and data professionals who are new to AI evaluation and want to build reliable systems. No advanced machine learning background is required. Start reading today to bring structure and confidence to your generative AI development.