How to Evaluate AI Beyond Fluency: Grounding, Answerability, and Reliability

This paper argues that fluency is no longer a sufficient basis for evaluating AI outputs. Large language models can produce polished, plausible, and well-shaped answers while remaining weakly grounded, poorly bounded, and unsafe to rely on in practice. Existing evaluation frames such as correctness, harmlessness, preference, and benchmark performance still matter, but they do not fully capture the human problem created by fluent systems: answers can feel complete before they are responsibly usable. The paper proposes three linked standards for judging AI outputs: grounding, answerability, and reliability. Grounding asks whether an answer is tethered to the prompt, evidence, context, and task constraints. Answerability asks whether the answer can be traced, challenged, limited, and revised under contact rather than protected by style or closure. Reliability asks whether a human can depend on the answer across contexts without hidden collapse. The paper argues that many important AI failures are forms of persuasive over-completion and offers a practical human standard for evaluating AI once fluency is cheap.