- Jan 29
- 7 min read
Vector embeddings are mathematical representations of content that LLMs and search engines use to assess relationships between different pieces of content. I’ve used vector-based techniques to audit and improve my clients’ content at iPullRank, often with immediate improvements in their AI search visibility KPIs.
In this article, I’ll cover:
For more of the latest AI search strategies, be sure to join us, and the top experts in search, at SEOWeek in New York April 27 - 30.
What are vector embeddings?
To truly understand vector embedding, first you should understand that search is an evolution that has moved on from simple keyword matching to something far more sophisticated: semantic understanding.
Vectors are numerical representations of words, phrases, or documents in a multi-dimensional space that capture semantic meaning. Vector embedding takes content, breaks sentences down, and turns them into numbers. Then, based on those numbers, two things that are saying the same thing will have similar representations in that multi-dimensional space.
Here's a classic example that demonstrates the power of vector embedding: If you take the vector representation of the word "king," subtract the vector representation for the word "man," and then add the vector representation for the word "woman," the closest match will be the vector representation for the word "queen." This mathematical operation reveals semantic relationships between words.
How AI search engines and LLMs determine relevance
When new content is collected by web crawlers and fed into LLMs, the text is first split into small blocks of characters, words or sub-words, called tokens.
Each token is turned into a numeric representation (a vector) that captures its meaning and maps relationships to other words and phrases (the embedding process), based on the correlations the model assigned during training. The model gains context when similar meanings cluster together.
The LLM treats phrases that are tightly clustered on its vector map as having high cosine similarity, which gives the LLM confidence that the document is about a specific topic. Cosine similarity is basically measuring the distance between angles in vector space:
If you get a cosine similarity close to 1, that means the content is really similar or highly relevant
If it's close to 0, that means it's not related
If it's close to -1, that means it's the opposite
This is how search engines and LLMs calculate relevance: by determining how close the vector representation of your document is to the vector representation of the user's query. The closer the vectors, the more relevant your content is considered to be. This is called a relevance score.
When users enter a prompt or query into an LLM‑powered system that uses vector search (like ChatGPT or Gemini), the query is turned into an embedding and used as the starting point. The system then looks for sources (pages, passages, documents) whose embeddings have high cosine similarity to the query, and uses those as the research base and context to generate a reply.
Why do vector embeddings matter for GEO?
The difference between how vector embeddings are used by search engines compared to LLMs is that LLMs use vector embeddings for all stages of the query response (prompt assessment, information retrieval, and generative response). Better alignment with this process brings more consistent results in generative engine optimization.
OpenAI states that vector embeddings can be used for a range of tasks, including:
Search (where results are ranked by semantic relevance to a query string, not just keyword overlap)
Clustering (where text strings such as queries or pages are grouped by similarity in meaning)
Recommendations (where items with related text strings are recommended because their embeddings are close)
Anomaly detection (where outliers with little relatedness in embedding space are identified)
Diversity measurement (where the spread of similarity across embeddings is analyzed)
Classification (where text strings are classified by the label whose embedding is most similar)
Now that you have a sense of vector embeddings and how they work, let’s address how to conduct a vector analysis so you can use the results to inform your content and search strategies.
How to conduct a vector-based competitor analysis
If you're a small business that hasn't even set up Google Search Console yet, you should cover your SEO basics before moving on to an advanced strategy like this one. Make sure you have a solid foundation.
Then, check out my tutorial "Vector Embeddings is All You Need: SEO Use Cases for Vectorizing the Web with Screaming Frog." Here, I provide step-by-step instructions for setting up Screaming Frog for vector analysis, including a custom GPT to help you write your code.
Vectorize and score your page
Start by vectorizing your page itself and seeing how well the page in general performs against your target query. If the scoring is low, the page isn’t relevant.
Break it down by sections
Search engines analyze pages in a fixed token size and layout-aware way, looking at boundaries like headers and paragraphs. They score individual paragraphs to figure out what parts of the page are most relevant to specific queries.
You want to determine your best paragraph for a given query, how it scores, and how it compares to your competitors. Once you’ve identified this paragraph, you can improve it using semantic triples, better readability, and clear data points. I dive deeper into content optimizations in the next section.
Compare against competitors
If your competitor is winning, vectorize both your page and theirs. Look at which paragraphs score best for the target query and identify the gaps.
At my agency, iPullRank, we build and analyze things at the component level. This allows us to understand not just which pages perform well, but which specific paragraphs or sections drive performance across different sites and queries. This granular analysis is only possible with vector-based approaches.
How to to improve your content with vector embeddings
Once you understand how your pages are performing, you can make these improvements and track the results.
Optimize for chunking
Chunking has two meanings. For the systems that use your content, it’s how they break it into passages for retrieval. For content optimization, chunking is the practice of structuring content into small, self-contained sections focused on a single idea, so it can be more easily processed and retrieved by search engines and language models.
When you write naturally, you'll often have a paragraph that's about four different things. This will hurt you in the vector space because the content meaning is diluted.
For example, if you have a paragraph arguing that the Showtime Lakers are the best team, you want to make sure that paragraph isn't talking about anything else except that specific idea. You can use cosine similarity scores to verify that each section has a single clear idea, thus optimizing it for surfacing in LLM responses, even though the Showtime Lakers are not the best team 😏.
Use semantic triples for clarity
A semantic triple is a sentence that uses a simple subject - predicate- object format.
For example: A chocolate chip cookie contains chocolate chips.
Not: Chocolate chip cookies are baked with pieces of chocolate throughout.
When you add semantic triples to your content that are informed by vector embeddings, then you can make content that LLMs can better understand and later show in conversational search. Usually, this type of content is easier for humans to understand, too.
Build authority through focused content
LLMs and other search engines see certain websites as experts on specific topics.
To determine this, they calculate average embeddings that represent all the content on your site, then compare each individual page to that average vector to determine the distance from what your site is primarily about.
What we've found is if you have content with low similarity scores—pages where you're randomly talking about topics unrelated to your core expertise—it hurts your authority. If you mostly talk about shoes but suddenly you're talking about bananas, search engines recognize you're not an expert on that idea. Prune those content outliers.
In other words, stay in your lane.
This has always been a quick win in our relevance engineering work. We identify these anomalies, remove or redirect them, and the client immediately sees the value because we're cleaning up their content and reinforcing their topical authority.
How to leverage vector analysis for technical SEO
Since RAG is a significant part of many LLMs, classic SEO should still form the foundation of your GEO activity. Vectorizing your content serves multiple use cases for SEO efforts that support GEO.
Internal linking structures
Once you have a vector database of all your content, figuring out the similarities and relationships is trivial. If you want to know the top 100 pages that are as close as possible to a given page, run a query that takes milliseconds. Now, you have a list of what your internal linking structure should be.
Redirect mapping
When you need to consolidate or redirect content, vector similarity helps you identify the best destination pages.
Content clustering
You can vectorize clusters of pages and then look at the mappings across clusters. This makes it easier to understand your content architecture and identify gaps or opportunities.
Query intent analysis
Look at all the citations from AI Overviews, look at all the keywords that pages rank for, and intersect those lists. For 20 different keywords, which pages appear most frequently? That's inherently part of understanding the query intent and what content best serves it.
Get started with vector embeddings today
If you're serious about generative engine optimization, understanding and implementing vector-based optimization isn't optional. It's how you ensure your content is discovered, understood, and valued by the systems that determine visibility in search.
If this sounds overwhelming, keep in mind that the fundamentals of good content creation—deep research, clear structure, focused topics, and comprehensive information—align with what these systems are looking for. You're not reinventing the wheel; you're being more intentional about how you engineer relevance into your content.
