LLM Evaluation: Building Reliable AI Systems at Scale

As LLMs move from prototypes to production, the challenge is building reliable AI systems that behave predictably under real-world conditions. Many teams can get a demo working, but struggle when systems face ambiguity, scale, and edge cases. This course focuses on building reliable AI systems by introducing a rigorous, evaluation-first approach to working with LLMs. I built this course from my work in adaptive AI systems and large-scale learning platforms, where I repeatedly saw the same failure pattern: teams relied on intuition and surface metrics rather than systematic evaluation. Models appeared to work, until they didn’t. The gap wasn’t in model capability, but in how systems were tested, monitored, and improved. This course distills those lessons into a structured framework for evaluating and stabilizing LLM-based systems. You’ll learn how to design evaluation workflows using traces, error analysis, and synthetic data to uncover real failure modes. The course covers practical strategies for evaluating prompts, RAG pipelines, and multi-turn agent workflows, while avoiding misleading metrics. You’ll also explore architectural best practices, such as separating evaluators from guardrails, and integrating evaluation into CI pipelines to ensure reliability as systems evolve. If your goal is building AI systems that are not just functional but dependable, this course gives you the tools and mindset to build reliable AI systems with confidence.