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The model does the work, not the code. The inference code should be generic autoregressive decoding that would work with any transformer checkpoint. If your generation loop contains addition-specific logic — manually pairing digits, threading carry state, indexing into specific positions — then the Python code is solving the problem, not the model.
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The next consideration is information density and specificity. AI models favor content that provides concrete, actionable information over vague generalizations or superficial coverage. This means investing in depth rather than breadth for your most important topics. A comprehensive 3,000-word guide that thoroughly addresses a topic will typically perform better in AI citations than ten shallow 300-word articles that skim the surface.