What Are Vector Embeddings? How AI Decides Your Content Is Relevant
Key takeaways
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You don't need to understand linear algebra to benefit from vector embeddings. You do need to understand one idea: AI measures relevance by meaning, not words.
Every time ChatGPT search, Perplexity or Google's AI features look for sources, they compare the meaning of the question with the meaning of thousands of passages and keep only the closest few. Your page could be beautifully written and well ranked. If none of its passages land close enough to the question, it never makes the answer.
Once that clicks, a lot of AI visibility advice suddenly makes sense, from chunking to information gain to why "comprehensive" content stopped working.
What are vector embeddings?
Vector embeddings are numerical representations of text, typically long lists of numbers, that capture meaning so that pieces of text with similar meanings have similar embeddings. AI search systems embed both the user's question and candidate passages, then retrieve the passages closest in meaning, which is how they find relevant content even when the wording differs. |
How vector embeddings work, without the math
Imagine a vast map where every passage of text gets a location. Passages about similar ideas land near each other. "How to get cited by ChatGPT" sits close to "earning AI citations" and far from "best running shoes."
An embedding model draws that map. It reads a passage and outputs its coordinates, often hundreds or thousands of numbers long. OpenAI's embedding models, for example, produce vectors with 1,536 or 3,072 dimensions. To find relevant content, the system embeds the question the same way and looks for the nearest passages, usually with a measure called cosine similarity.
The idea isn't new. Google researchers popularized word embeddings with word2vec in 2013, famous for showing that the vector math of "king" minus "man" plus "woman" lands near "queen." Modern models embed whole sentences and passages, but the principle is the same: meaning becomes geometry.
Where embeddings show up in AI search
Embeddings power the retrieval step in retrieval-augmented generation, the Filter stage where candidate passages get narrowed to the few the model actually reads. They're also the backbone of semantic search.
That filter is where most content loses. Kevin Indig's analysis of about 1.2 million ChatGPT responses found it retrieved roughly six times more pages than it cited, Search Engine Land reported. Being fetched is easy. Being among the closest passages to the question is what gets you used.
And the questions are getting more specific. Peec's analysis of over 20 million ChatGPT fan-out queries found their average length roughly doubled between October 2025 and January 2026, from about 6 words to about 12. Longer, more precise queries land in more precise places on the map, which rewards precise passages.
The crowded neighborhood problem
Here's the single most useful idea in this post. Generic content lives in a crowded neighborhood.
A paragraph that says "organizations must leverage innovative solutions to drive results" lands in the busiest part of the map, surrounded by thousands of nearly identical paragraphs. It's a little bit close to many questions and the closest to none. When a specific question comes in, something more specific is almost always closer.
A passage that names a framework, gives a number or describes a concrete method lands somewhere distinct. When a question asks about that thing, it's the nearest match by a wide margin. That's the retrieval case for information gain: original content doesn't just impress readers, it occupies empty space on the map.
Passage | Where it lands | Retrieval odds |
"Content marketing helps businesses grow by engaging audiences." | The most crowded neighborhood | Low for almost any specific question |
"Share of model is the percentage of AI answers in a category that name your brand." | A distinct spot near "share of model" questions | High for that question |
"SparkToro found 68.01% of US Google searches ended without a click in early 2026." | A distinct spot near zero-click questions | High for zero-click questions |
How to write passages that win retrieval
One idea per passage
A section covering three ideas gets an embedding that's a blur of all three, close to none of them. Keep each section on one question. That's content chunking for AI.
Say the specific thing
Names, numbers, examples, defined terms and named frameworks make a passage distinct. Vague claims make it generic.
Use the buyer's framing
Embeddings capture meaning, but your framing still shapes where you land. If buyers ask about "getting recommended by ChatGPT" and you only write about "LLM brand affinity," you're near the question but not on it. AI search prompts covers how to find real buyer language.
Lead with the answer
The opening of a passage carries a lot of its meaning. Put the direct answer first, as in BLUF writing and AI snippets.
Do embeddings make keywords irrelevant?
No. Many systems combine keyword matching with embeddings, often called hybrid search, and AI assistants frequently run ordinary web searches before retrieval, as covered in search grounding. Keywords still help you get into the candidate pool. Embeddings decide which passages from that pool get used.
So the old advice survives in a new form: use the words your buyers use, but stop repeating them. Covering the topic well does more than saying the keyword again. How many keywords per page and what is NLP in SEO go deeper.
A quick embeddings audit for your content
Pick a target question. Write it exactly the way a buyer would ask it.
Find your best passage. The single paragraph on your site that answers it.
Ask: is it specific? Does it name the thing, give a number or describe a method?
Ask: is it alone? Does it make sense without the paragraphs around it?
Ask: is it different? Would ten competitor pages say roughly the same sentence? If so, it's in the crowded neighborhood. Add what only you know.
Then test it live by asking AI assistants the question and checking whose passage gets quoted. Track the results in your share of model.
Vector embeddings in the Engine Optimization Matrix™
Embeddings sit under the Content column of the Engine Optimization Matrix™, especially the GEO row: multi-paragraph structured answers and named frameworks that land as distinct, retrievable passages. They also explain why the Citation column is passage-level, not page-level.
Related reading: what is generative engine optimization, ranking but not cited, query fan-out and what is a knowledge graph.
Buyers are asking ChatGPT for a shortlist. The Demand Doctrine™ for GEO is how you get on it: structured answers, the right off-site presence and the signals generative engines trust. Out November 18. Want all four engines? Mastering AI Visibility.
Frequently asked questions
What is a vector embedding?
A vector embedding is a list of numbers that represents the meaning of a piece of text, so similar meanings get similar numbers.
How do AI search tools use embeddings?
They embed the user's question and candidate passages, then retrieve the passages whose embeddings are closest in meaning to the question.
Are embeddings the same as keywords?
No. Keywords match words. Embeddings match meaning, so a passage can match a question that uses entirely different wording.
What is cosine similarity?
It's a common way to measure how close two embeddings are, based on the angle between them. Closer angles mean more similar meaning.
Why does generic content struggle in AI search?
Generic passages sit in a crowded part of the meaning map, close to many topics but closest to none, so more specific passages usually win retrieval.
Do keywords still matter if AI uses embeddings?
Yes, partly. Many systems combine keyword and embedding search, and assistants often run keyword-based web searches before choosing passages.
What is the difference between embeddings and semantic search?
Embeddings are the representation of meaning. Semantic search is the approach of ranking results by meaning, often using embeddings.
How do I make my content easier to retrieve?
Keep one idea per section, lead with the answer, use specific names and numbers and write the way your buyers describe the problem.
About the author. Harold Bell is the founder of MQL Magnet and has spent about 16 years in enterprise technology marketing, working with brands like Cisco, Rubrik and Nutanix in different capacities. He's a member of the Forbes Communications Council, the creator of the Engine Optimization Matrix™ and the author of Partner Over Product. |
