LLM applications fail silently at the semantic level. Standard unit tests verify that functions return values, but they cannot detect if the output is factually wrong, off-tone, or missing steps. Evals fix this by running a prompt, inspecting the output, and programmatically or judgmentally deciding its quality. Why LLM Testing is Different Unit tests break on non-determinism. Temperature-driven randomness means identical inputs produce varying valid outputs, which defeats static...