The Ontology-Based Contextual AI Evaluation (OB-CAIE) methodology addresses the lack of scientific rigor caused by ambiguous test coverage, balances human expertise with automation, and improves reproducibility of evaluation environments.\ \ OB-CAIE strengthens current AI evaluation practices by explicitly defining what is to be tested, the first step of the scientific method. The approach is built on two ontologies: the Domain‑Specific Ontology (DSO) that specifies what to evaluate, and the Evaluation Process Ontology (EPO) that specifies how to evaluate.\ \ Together, DSO and EPO delineate a tractable problem space that can support one or multiple AI evaluations. Human judgment is incorporated at scientifically justified points, especially in complex domains where feedback is irreducible or machines are insufficient.\ \ A key benefit of OB-CAIE is that failure points can be traced, visualized, and analyzed within its canonical problem space, enabling transparent and debuggable evaluation pipelines.\ \ Review: This framework’s ontology‑driven modeling clarifies evaluation scope and ensures process traceability, offering a structured path toward reproducible AI assessment.