Navigating the Divergence Between SEO and LLM Guidance
LLM Guidance Doesn’t Transfer The Way SEO Guidance Did
Search Engine Journal
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The guidance systems for search engine optimization (SEO) and large language models (LLMs) differ significantly, undermining the previously reliable portability of SEO strategies. Unlike SEO, where major search engines collaborated on standards, LLMs operate independently, leading to unique practices and outcomes across platforms. This shift requires practitioners to adapt their strategies to account for the distinct nature of each LLM provider.
- 01SEO guidance was built on collaborative standards among major search engines, ensuring portability across platforms.
- 02LLMs like OpenAI's ChatGPT and Google's Gemini operate on different training data, retrieval systems, and alignment processes, leading to inconsistent outcomes.
- 03The proposed llms.txt file, intended to guide LLMs to important content, has not been adopted by any major LLM provider.
- 04Google's AI surfaces, such as AI Overviews and AI Mode, do not consistently reflect the same guidance as its traditional search results.
- 05Only 11% of cited domains appeared across multiple LLM platforms, indicating a significant divergence in content visibility.
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The landscape of guidance for search engine optimization (SEO) has historically relied on a collaborative framework among major search engines, allowing practitioners to optimize for one platform with the confidence that it would apply across others. This was exemplified by shared protocols like Sitemaps and Schema.org. However, the emergence of large language models (LLMs) such as OpenAI's ChatGPT and Google's Gemini has disrupted this model. Unlike SEO, LLMs operate on distinct training datasets and retrieval systems, resulting in a lack of standardization and portability. For instance, the proposed llms.txt file, designed to help LLMs identify crucial content, has not been adopted by any major provider, highlighting the absence of a shared standard. Furthermore, analyses reveal that Google’s AI surfaces do not align with its own SEO guidance, complicating the optimization landscape. As a result, content that performs well on one platform may not translate to others, necessitating a more nuanced approach for practitioners. They must now treat guidance from each LLM provider as a singular input rather than a comprehensive roadmap, adapting their strategies to the unique characteristics of each platform.
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