Context Relevance tells you whether retrieval handed the model the right chunks, so you can catch a bad answer at its source instead of blaming the prompt.
A RAG evaluation model scores retrieval and generation separately, so you catch bad answers in testing instead of hearing about them from an annoyed user.
A RAG pipeline turns documents into answers in four stages, and every stage is a chance for retrieval and permissions to fail while everyone blames the model.