You are explaining to a stakeholder why executing the same Claude prompt twice may yield slightly different results. The stakeholder is worried that this indicates the application is broken. How would you respond to the stakeholder’s concern?
A Tell the stakeholder the variation is caused by Claude being updated continuously by Anthropic, and that switching to a fixed model snapshot will eliminate the variation entirely. B Tell the stakeholder the variation comes from network latency and that switching to a faster network connection will produce more consistent results across runs. C Tell the stakeholder the variation is a bug that the team will fix in the next release of the application, then create a work ticket to fix the bug. D Explain that LLMs are non-deterministic by default due to sampling, and describe how the application handles this through validation, retries, or temperature adjustment. Show Answer Answer Explanation LLM generation is probabilistic: sampling can select different plausible tokens for identical inputs, so minor variation across runs is expected and does not by itself indicate an application defect. Production applications should manage this variability with measures such as output validation, retries, and—where supported by the selected model—sampling controls such as temperature. Anthropic notes that API outputs can remain non-deterministic even at temperature 0.
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