LLM Evaluation: Building Reliable AI Systems at Scale

This course provides a roadmap for building reliable, production-ready LLM systems through rigorous evaluation. You’ll start by learning why systematic evaluation matters and how to use traces and error analysis to understand model behavior. You’ll build an evaluation workflow by capturing real failures and generating synthetic data for edge cases. You’ll avoid traps like misleading similarity metrics and learn why simple binary evaluations often beat complex numeric scales. You’ll also cover architectural best practices, including where prompts fit and how to keep guardrails separate from evaluators. Next, you’ll evaluate complex systems in production: scoring multi-turn conversations, validating agent workflows, and diagnosing common RAG failure modes. You’ll also learn how tools like LangSmith work internally, including what they measure and how they compute scores. By the end, you’ll integrate evaluation into development with CI checks and regression tests to keep AI stable as usage and complexity grow.