Chapter 7 — Testing the Probabilistic - The AI Evaluation Pipeline

Chapter 7 focuses on establishing a rigorous evaluation discipline for probabilistic AI systems, recognizing that traditional software testing alone cannot adequately measure AI behavior. It introduces golden and regression datasets, synthetic test generation, deterministic assertions, LLM based evaluation, human evaluation, and specialized approaches for assessing models, RAG, agents, safety, and prompts. It also defines key evaluation dimensions such as context precision, context recall, faithfulness, answer relevance, and task success, bringing them together in the Enterprise AI Evaluation Framework to make AI quality measurable, repeatable, and continuously testable.