Google representatives stated that creating "bite-sized" content specifically for large language models (LLMs) like Gemini will not improve search engine ranking. In a recent episode of Google's "Search Off the Record" podcast, John Mueller and Danny Sullivan addressed the increasingly popular SEO practice of content chunking, where websites break down information into smaller paragraphs and sections, often with numerous subheadings formatted as questions.
The intent behind content chunking is to make it easier for generative AI bots to ingest and cite the information, theoretically boosting search visibility. Websites employing this technique often feature short paragraphs, sometimes consisting of just one or two sentences, designed to cater to AI algorithms. However, Sullivan clarified that Google's search algorithms do not use these signals to improve ranking. "One of the things I keep seeing over and over in some of the SEO advice is that you should break things down into these really bite-sized chunks," Sullivan said. "And that's not something that we look at."
Search engine optimization (SEO) has become a significant industry, with businesses constantly seeking ways to improve their website's visibility in search results. While some SEO practices are legitimate and beneficial, many others are based on speculation and unproven theories. The rise of LLMs has led to new SEO strategies aimed at optimizing content for AI consumption, but Google's statement suggests that these strategies may be misguided.
The implication of Google's stance is that content creators should prioritize creating comprehensive and well-structured content for human readers, rather than attempting to game the system by creating fragmented content for AI. This aligns with Google's long-standing emphasis on providing users with high-quality, relevant search results. The development highlights the ongoing tension between optimizing content for algorithms and creating valuable content for human users. As LLMs continue to evolve and play a larger role in information retrieval, the debate over how to best optimize content for both AI and humans is likely to continue.
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