Last Updated on April 19, 2026 by Aimee Jurenka
From SEO to a Buzzword Betty
AI search didn’t just change how users define content; it completely rewired how I think about organizing it.
Somewhere between Google’s SGE rollout, LLM-powered chat results, & “what even is a vector database,” I found myself deep in the weeds of tech terms I never planned to learn. Yet, here I am, a full-on Buzzword Betty, decoding machine learning jargon because I want to understand how LLMs actually retrieve & resurface content.
These concepts: vector embeddings, cosine similarity, & semantic similarity, aren’t new to the industry. Folks in machine learning, AI, & NLP SEO have been using them for years.
But they’ve just now become too relevant for me to ignore any longer.
If you’re trying to figure out how to get your content in front of users in an AI-powered world, they’re probably relevant to you too.
This post breaks down the three terms that I glommed onto when I first started thinking about what AI-search is & how it is going to change my j-o-b.
TL;DR: How AI Organizes Content (& How You Can Too)
Vector Embeddings → What it does: Labels your content with numbers that represent meaning → Strategy tip: Break content into clean, focused chunks
Cosine Similarity → What it does: Compares those numbers to find related content → Strategy tip: Use it to identify duplicates, content clusters, & outliers
Semantic Similarity → What it does: Understands how content relates in meaning → Strategy tip: Reorganize around topics, themes, & search intent by cosine score
Vector Embeddings
Before a machine can understand your content, it needs to label it. Vector embeddings do exactly that, they turn your words into math so machines can compare what you’ve said to everything else they know.
What They Are
Vector embedding is the process of assigning a number to your content.
Let’s say that again because it’s important: It’s labeling your words, sentences, paragraphs (chunks), or full pages with a number.
Those numbers, called vectors, represent the meaning of the content. And because numbers are math-friendly, machines can then use them to compare, retrieve, & organize ideas.
Words & phrases with similar meanings get similar vector labels (numbers). So “coffee shop” & “café” might be close in the vector space, while “coffee shop” & “satellite” are far apart.
How LLMs Use Them
Large Language Models (LLMs) like ChatGPT & Gemini use vector embeddings to:
- Label your content with vector numbers
- Store those numbers in a vector database
- Use them later to compare with new queries (we’ll get to that next)
It’s how AI organizes the world of language.
Cosine Similarity
Once your content is labeled with numbers (via vector embeddings), the next question is: Which content is most similar to this new question?
That’s where cosine similarity comes in.
What It Is
Cosine similarity is how machines compare the vectors you’ve created. It looks at the angle between two vectors in space:
- 1 = exact same direction → super similar
- 0 = totally unrelated → no match
- –1 = opposite direction → opposite meanings
How LLMs Use It
After embedding your content & queries, LLMs:
- Embed your query into a vector
- Compare it to stored vectors using cosine similarity
- Find the closest matches (top cosine scores)
- Retrieve or remix those chunks to respond to your prompt
This is how RAG (Retrieval-Augmented Generation) works: Label with vectors → Compare with cosine → Respond with relevance
Semantic Similarity
Semantic similarity is the core of how modern AI understands relationships, not just between words, but between ideas.
This is what turns labeled & compared content into something meaningful for search, chat, & context-aware experiences.
What It Is
Semantic similarity is the process of determining how close two pieces of content are in meaning, not just wording.
It builds on:
- Vector embeddings (labeling content by meaning)
- Cosine similarity (comparing that meaning)
- And powers the tools that understand NLP, context, & knowledge graphs
How LLMs & Search Engines Use It
LLMs label your content using vector embeddings. They then compare that labeled content using cosine similarity. To then organize & retrieve content based on semantic similarity.
This is what makes a chatbot feel smart, what powers AI search summaries, & what tells Google that your FAQ page actually answers the user’s question.
What To Do With This
If this is how AI is organizing and retrieving our content, then it only makes sense to create and optimize with that system in mind. The closer we align with how machines interpret meaning, the more likely our content is to be found, understood, and served up in the right moments.
Vector Embeddings + Cosine Similarity → Organizing Content for Relevance
Find Duplicate & Overlapping Content: Use semantic matching to uncover pages that are saying the same thing in slightly different ways. Whether it’s accidental repetition, cannibalization, or just multiple versions of the same idea, these overlaps can clutter crawl paths & dilute relevance.
Surface Off-Topic & Low-Signal Pages: Spot content that drifts off-theme pages that don’t support your core topics or dilute your topical authority. Great candidates for pruning, merging, or reworking into something more focused.
Map Out Your Content Clusters: Get a high-level view of how your content naturally groups together. Semantic similarity helps you visualize which topics are well-covered & where your content stands alone (for better or worse).
Pro tip: You can do all three of these steps with Screaming Frog’s SEO Spider using their semantic similarity reporting. It’s a fast way to move from insight to action, especially on large sites.
Tools & Thinkers to Follow
- Noah Learner – The guy who was nice enough to explain this to me week after week till it clicked
- Mike King – Vector Embeddings is All You Need: SEO Use Cases for Vectorizing the Web with Screaming Frog
- OpenAI Embedding API – if you want to go deeper on the what & how to use them in your work