LLM Evaluation and Testing: Shipping Reliable AI

A premium, complete and advanced course on evaluating and testing LLM-powered systems, updated for 2026. You will learn why evals are the hardest and most important discipline in AI engineering, the full evaluation taxonomy (offline vs online, reference-based vs reference-free), why classic metrics such as BLEU, ROUGE and exact match fail on open-ended text, and how embedding-based metrics help and mislead. You will master LLM-as-a-judge (rubric design, position/verbosity/self-preference bias, calibration against humans), building golden datasets with edge cases and slices, and the real 2026 toolchain: promptfoo, LangSmith, Langfuse, Braintrust, DeepEval, Ragas and pytest. The course goes deep on evaluating RAG (retrieval and generation), evaluating agents (task completion and trajectory), safety and guardrail evals, red-teaming, regression testing prompts in CI/CD, online A/B testing, quality-drift monitoring, human annotation and the real cost of evaluation. Includes runnable code, production patterns, a strong focus on data privacy and GDPR for eval data, and a comprehensive final assessment. Educational content, not legal advice.