The video explains that Large Language Models (LLMs) use vector embeddings to map and understand the contextual relationships between topics on a website, enabling more sophisticated content interpretation beyond keywords. For marketers, leveraging these vector maps helps identify content gaps and improve site relevance in AI-driven search, though exact user search queries remain difficult to determine.
The video explains how Large Language Models (LLMs) work by translating vast amounts of data, such as user preferences and site content, into vector data. Vector data consists of coordinates that represent topics on a conceptual map. For example, a website selling shoes would have the topic “shoes” mapped to a specific coordinate, surrounded by related topics also mapped in this vector space. This mapping allows LLMs to understand the context and relationships between various topics on a site, rather than just isolated keywords.
LLMs use these vector embeddings to create a three-dimensional map of topics, showing how closely related different concepts are. This contextual understanding enables the model to interpret content in a more humanlike and sophisticated way, even though it does not literally understand the content visually. Instead, it relies on numerical relationships between topics to grasp the overall meaning and context of the information presented on a website.
For marketers, understanding how LLMs interpret content through vector embeddings is valuable. By analyzing the vector coordinates of topics covered on their site, marketers can gain insights into how AI perceives their content and identify potential gaps. Tools like TensorFlow’s projector and Screaming Frog integrated with ChatGPT API can help visualize these topic maps, revealing what topics the LLM associates with the site and highlighting areas that may need more content development.
While this vector embedding visualization provides useful insights, it has limitations. It does not show exactly what users are searching for or how the site is found in search results. Instead, it offers a perspective on what topics the site might be found for based on the AI’s understanding. For more actionable insights on content gaps and opportunities, marketers can use tools like Semrush and SparkToro, which analyze competitive content and audience interests to suggest areas for content expansion.
The video also notes that there is currently no reliable way to determine the exact search prompts or queries that lead users to a site through LLMs. Attempts to reverse-engineer this data are speculative at best. Overall, the key takeaway for marketers is to focus on creating comprehensive, contextually rich content that covers a range of related topics, thereby improving how LLMs understand and rank their site in AI-driven search environments.