What Happened
Duane Forrester, Vice President of Industry Insights at Yext, published an article on Search Engine Journal discussing how large language models (LLMs) remove signals that users once used to judge the quality of content. These signals, such as author bylines, publication dates, and source credibility, are often stripped away in AI-generated answers, making it harder for users to assess the reliability of the information they receive.
The Specifics
Forrester argues that LLMs act as "time machines" by presenting information without context, such as when the content was published or who wrote it. This lack of context can lead users to trust outdated or unverified information. The article highlights how this shift impacts both users and content creators, as traditional methods of evaluating content are no longer available.
Why This Matters to SEO Practitioners
For SEO practitioners, this development means that the signals they once relied on to build trust and authority are no longer visible to users. Content that was once judged by its source, author, or publication date is now presented in a vacuum. This shift requires a rethinking of content strategies to ensure that the information provided is not only accurate but also presented in a way that users can still trust.
Actionable Step
One concrete step that SEO practitioners can take is to focus on creating evergreen content that remains relevant over time. By ensuring that the information is timeless and regularly updated, practitioners can mitigate the impact of LLMs stripping away contextual signals. Additionally, emphasizing the credibility of the source within the content itself can help users understand the reliability of the information, even if the AI-generated answer does not explicitly state it.
