Evaluating Generative AI: Metrics and Frameworks for LLM Applications

Deploying generative AI is only the first step; ensuring its outputs are reliable, safe, and accurate is the real challenge. Without systematic evaluation, large language model applications risk hallucinating, leaking data, or delivering poor user experiences. This course equips you with the foundational knowledge to design and implement robust evaluation strategies for generative AI, helping you transition from manual testing to automated, metric-driven frameworks. What you'll learn: - Understand foundational evaluation terminology and the unique challenges of testing non-deterministic AI models. - Apply quantitative and qualitative metrics to assess text generation, summarization, and translation quality. - Implement Retrieval-Augmented Generation (RAG) evaluation patterns using context relevance and faithfulness. - Explore the LLM-as-a-judge paradigm for automated, scalable qualitative feedback. - Identify and mitigate security, bias, and safety risks through targeted evaluation methodologies. - Configure continuous monitoring and observability workflows to track production performance. The course begins with core definitions and the unique hurdles of AI evaluation before introducing specific benchmark metrics. You will then progress to advanced patterns like automated evaluation and production monitoring through clear, written explanations and practical examples. Designed for beginners, developers, and product managers entering the AI space, this course requires no advanced mathematical background or prior machine learning experience. Start reading today to build trust and reliability in your generative AI applications.