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.
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In recent years, LLMs have shown significant improvements in their overall performance. When they first became mainstream a couple of years before, they were already impressive with their seemingly human-like conversation abilities, but their reasoning always lacked. They were able to describe any sorting algorithm in the style of your favorite author; on the other hand, they weren't able to consistently perform addition. However, they improved significantly, and it's more and more difficult to find examples where they fail to reason. This created the belief that with enough scaling, LLMs will be able to learn general reasoning.