Unverified50% confidenceTradeoffExact time
Code cells yield the same result every time they are executed under a fixed random seed, whereas a pure 'prompt → result' approach introduces the non-determinism of large models.
1
Sources
50%
Confidence
Long-term
Relevance
10/10/2026
First Seen
Sources
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Related Claims
UnverifiedReverse-engineered prompt results may not 100% reproduce the original image, because the original prompt is highly dependent on a specific model version and random seed.78% similarUnverifiedWhen mainstream code generation models receive ambiguous instructions, their default behavior is often to select the highest-probability interpretation and generate output directly, which is prone to hallucinations and erroneous code.77% similarUnverifiedIn AI content generation, the seed value determines the initial noise distribution; the same seed value combined with the same prompt can theoretically produce consistent results; setting the seed value to -1 means zero randomness, relying entirely on the prompt and reference image to determine the output.76% similarUnverifiedOutput examples are key to suppressing randomness and hallucinations in large models: without examples responses may be irregular, but once a formatted reference is provided the output will closely follow the expected template.75% similarUnverifiedDeterministic systems given the same input always produce the same output, whereas deep learning-based AI systems are inherently probabilistic systems that may produce different outputs from the same input due to factors such as temperature parameters and random seeds.74% similar
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