Which statement best describes what each of the three LLM evaluation modes covers?

Evaluating a language model is harder than grading a math quiz. There is rarely one correct string to match against, because quality is multi-dimensional: is the answer faithful, relevant, complete, and in the right tone? No single tool measures all of that, so modern teams use **three complementary modes**. First, automated metrics. These are cheap, fast, and deterministic checks for things with a clear right answer: did the code run, is the JSON valid, does the output match the gold label? Great for what is auto-checkable, useless for nuance. Second, LLM-as-judge. Here a strong model scores another model's output on fuzzy qualities like helpfulness or clarity. It scales to thousands of examples cheaply and handles judgments that need general intelligence, though it carries known biases. Third, human evaluation. Slow and expensive, so you only spot-check, but humans are the ground truth for high-stakes or domain-specific calls a model cannot reliably make. The key idea: they are complementary, not competing. Each catches failures the others miss, so good teams run all three rather than betting on one.

The correct framing is that the three modes have **complementary coverage**, not interchangeable coverage. Automated metrics handle auto-checkable properties cheaply and deterministically. Examples include code execution, exact match on a gold label, schema validation, and string overlap scores. They are fast and unbiased, but blind to nuance. LLM-as-judge handles the fuzzy middle. A strong model scores qualities like helpfulness, coherence, or tone that resist hard coding. It scales to thousands of examples for cents each. The tradeoff is known bias: position bias, self-preference, and weak factual grounding. Human evaluation is the ground-truth anchor. It is slow and costly, so you only spot-check, but humans catch domain-specific and high-stakes errors a model misses. Human labels also calibrate whether your judge is trustworthy. Why not just pick one? Because each mode catches a different failure class. An automated metric cannot grade tone. A judge cannot reliably verify niche medical facts. A human cannot review a million outputs. Running all three is defense in depth, not redundancy. The wrong options confuse this. The modes are not interchangeable, human review does not scale, and a judge should usually not be the same family as the model it grades.