There is no doubt about it, large language models excel with respect to the use of language, the flooding of newly written material and endlessly chatty chatbots can attest to that. And yet, one of the more powerful features that LLMs bring have to do with how they operate under the hood in combination with another type of model, embedding models and their vectors.
Vectors are essentially a way these models represent words. An embedding model takes words and transforms them into fixed length vectors whose relative positions encode meaning; so that “cat” and “kitten” are geometrically closer than “cat” and “cactus”. Now, these vectors are embedded in a high-dimensional space, not 4, not 5, but hundreds if not thousands of dimensions. That means an enormous amount of semantic nuance is captured by these models. With language grounded in geometry, systems can rapidly cluster, rank, and compare ideas; LLMs then draw on such internal representations to spin those concepts back into words and sentences we can all understand.
But what does this have to do with SEO?
Well, at its core, SEO attempts to use patterns in how people ask questions and how pages answer them. Classic SEO looked at that with surface signals, things like exactmatching keywords, backlinks, meta tags. Embeddings let us dive a layer deeper: we can measure meaning itself.
Query–content matching in the same space
Think back to the “cat” versus “kitten” versus “cactus” example. When these words are represented as vectors (and thanks to the large encoding databases), we can mathematically see that cat is closer to kitten than it is the cactus. Now, with context to SEO, imagine encoding both user queries and every candidate page into vectors. The relevance of each pages’ answer to the query becomes a simple distance computation. If your article on “lowmaintenance indoor succulents” lives nearer to the query vector than competitors’ pages, you deserve the click, even if you never used the exact phrase “easycare houseplants.” Truly modern search engines are working this way (think Google’s BERT, MUM, or OpenAIpowered site search). With vectoraware SEO tools, we are now able to preview the semantic gap between content and potential queries before publishing.
Keyword clustering
Another great feature that has been born out of these embedding models is the ability to run efficient clustering. This means we can dump tens of thousands of longtail keywords into an embedding model, run a straightforward clustering algorithm, and see natural topic clusters pop out: “watering problems”, “pest control”, “desksize plants”. Each cluster topic in a way recommends a pillar page, plus supporting subpages, allowing us to build smarter website structures! Website optimization involves structuring content in this top-down general to specific way, and with embeddings, we can visualize the relationships between all the parent-child nodes the entire way down the tree. By ensuring the similarity vectors are closest between parent-child nodes, we are strengthening semantic coherence and helping crawlers understand topical authority.
Contentgap vectors
Let’s dive into this query-answer relationship a bit more. Using these embedding vectors, we can plot your existing articles in the same space as all search queries in your niche. Think of the result as a kind of heat map. The areas with high concentration of both query and returned pages are well covered by your current content, but the regions where you have clustered queries, but no content, highlight opportunities more objectively than intuition ever could. This method allows for precise edits of existing content and a clear roadmap to follow in terms of new content.
Continuous relevance monitoring
Semantics drift. As jargon evolves (the concept of “generative AI” replacing “GANs,” for instance), you can routinely recalculate embeddings and flag pages whose distance to their primary intent has grown. Even better, you can automate this process and cue a quick refresher before your ratings slip.
In short, thinking in vectors shifts SEO from stringmatching to geometric engineering. By speaking the same mathematical language as the engines, and futureproofing in the process, you spend less time guessing what “the algorithm” wants and more time publishing the obvious answer, no matter how the question is phrased. In this new world, vectors don’t replace keywords; they reframe them. The winners capture and map the intent, not just the terms, ranking will become more about meaning and less about repetition.
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