An AI model must avoid repetition. Repetition happens when an AI model reuses ideas without changing them enough. The AI model must add small differences to make each idea feel new.
By Pawan Nayar · from the LLOS AI Learning record
An AI model must avoid repetition.
Repetition happens when an AI model reuses ideas without changing them enough. The AI model must add small differences to make each idea feel new.
An AI model feels repetitive if its small differences are weak. The AI model must use different types of things. The AI model must change how it speaks. The AI model must use different sizes or amounts of things. The real problem is when the AI model is lazy with meaning. The AI model must not just use similar words. The AI model must make sure the user feels like they are seeing new ideas.
A developer asks an AI model to create many images of trees. If the AI model only shows trees that look the same, the user will get bored. The AI model should show different kinds of trees. It should show trees in different seasons. It should show trees from different angles. This makes each image feel new.
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An AI model should only avoid themes if the user specifically asks for it. A list of banned words means certain words cannot be used.
An AI model should show additive numbers for user progress. These numbers always go up.
A developer should define the full range of what an AI model can do. These are like the edges of a playing field.
A developer can use a special admin console. This console is a simple web page that connects to the AI model's server.
An AI model must avoid repetition. Repetition happens when an AI model reuses ideas without changing them enough. The AI model must add small differences to make each idea feel new.