An AI model copies the shared patterns in examples, even when instructions ask for variety. Adding more rules to a prompt makes output more rigid, not less. To get variety, provide examples that are truly different from each other.
By Pawan Nayar · from the LLOS AI Learning record
An AI model copies the shared patterns in examples, even when instructions ask for variety.
Adding more rules to a prompt makes output more rigid, not less. To get variety, provide examples that are truly different from each other.
Adding more rules to a prompt does not create variety. The AI model will follow the new rules, making the output rigid in a different way. More rules become a new pattern for the AI model to copy. The real solution is to change the structure of the prompt. Generate each piece of output in a separate request. Or, remove all variety instructions. Give the AI model one small, clear task. Less structure often leads to more variety. Bad examples are a common cause of repetitive output. If all examples have the same start, length, or structure, the AI model will copy that shared shape. This happens even if the instructions tell the AI model to be varied. To fix this, rewrite the examples. Make sure they are truly different. They should have different first words, different lengths, and different ways of starting an idea. Do not label them with 'move names'.
A developer wants an AI model to write varied social media posts. The developer includes many rules for different post types. The AI model produces posts that follow the rules but still sound very similar. The developer then removes most rules and provides three example posts that are very different in length and style. The AI model then generates much more varied posts.
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An AI model copies the shared patterns in examples, even when instructions ask for variety. Adding more rules to a prompt makes output more rigid, not less. To get variety, provide examples that are truly different from each other.