Summary: One output cannot establish how well an AI system performs. Evaluate with multiple representative inputs, repeated runs, and confidence intervals.
Suppose you ask an AI customer-service system the same question several times: “Can I return an opened product after 30 days?” One answer may explain the policy accurately. Another may omit an important exception. A third may confidently promise a refund that the customer is not entitled to receive. Which of these answers represents the system? The answer is: all of them, taken together — and none of them, taken in isolation.
Yet teams often evaluate AI systems by running them once, inspecting the result, and drawing conclusions about what AI can do or how it should be used. This is not because they are lazy or careless. It is because decades of deterministic software have taught us that a feature that works once will work the same every time. AI systems offer no such guarantee. One good output demonstrates that a system can perform a task. It does not show how often or how reliably the system will do so.
AI Outputs Are NondeterministicA nondeterministic system can produce different outputs when given the same input.
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