Vector Embeddings in SEO: Definition, and What You Can Actually Control

In the news: Since 2026, Google's embedding model is natively multimodal: gemini-embedding-2 maps text, images, video, audio and documents into a single shared embedding space across more than 100 languages, with output dimensions selectable between 128 and 3072, where earlier text embedding models handled text alone (Google, Gemini API embeddings documentation, April 2026).

Vector embeddings are the least glamorous and most consequential thing in modern search. They are also the subject of a great deal of confident advice that falls apart on contact with the vendors' own documentation. This page defines the term precisely, shows what the numbers physically are using figures published by Google and OpenAI, and then does the thing most treatments avoid: explains why you cannot optimize an embedding, what peer-reviewed research proves the mechanism cannot do, and what that leaves you.

Vector embedding, defined in one line

A vector embedding is a piece of content converted into a list of numbers, positioned in a learned space so that content meaning similar things lands in similar places. Retrieval then becomes a question of proximity rather than word overlap. In SEO terms: your page is turned into coordinates before anything decides whether to show it.

The definition is worth taking literally, because the literal reading is where the useful consequences live. OpenAI's documentation states it without ceremony: "An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness." That is the whole object. Not a summary, not a tag, not a category. A row of decimals.

What makes it powerful is that the positions are learned rather than assigned. Nobody decided that a page about roof insulation should sit near a page about heating bills. A model trained on an enormous amount of text arranged the space so that they do, because in the text it saw, those things travel together. This is why a query phrased in words that appear nowhere on your page can still retrieve it, and why two pages sharing a vocabulary but discussing different things can end up far apart.

What the numbers actually are

Typically between a few hundred and a few thousand floating point numbers. OpenAI's text-embedding-3-small outputs 1536 values and text-embedding-3-large outputs 3072. Google's gemini-embedding-2 lets you choose anywhere between 128 and 3072. Each value is a coordinate on one learned axis, and no individual axis has a human-readable meaning.

People often expect the dimensions to be interpretable, as if position 412 were "formality" and position 1108 were "about finance". They are not. The axes emerge from training and are, individually, meaningless. Only the geometry of the whole vector carries information. This matters practically, because it kills the idea that you could reverse engineer which words move which dimension and write accordingly.

That last row is the change worth registering. Google's Gemini API embeddings documentation describes gemini-embedding-2 as mapping text, images, video, audio and documents into a unified embedding space across more than 100 languages. A shared space means a diagram and the paragraph explaining it are no longer separate assets in separate systems. They are two points that can sit next to each other, and a query can retrieve either. For anyone publishing, that quietly promotes images and video from decoration to retrievable material, and makes accurate media a retrieval question rather than an accessibility afterthought.

How similarity gets computed

Usually cosine similarity, which compares the angle between two vectors rather than their length. OpenAI recommends it and notes the choice of distance function typically does not matter much, because their embeddings are normalized to length 1, which makes cosine similarity produce identical rankings to Euclidean distance while computing faster as a dot product.

The angle detail is not pedantry, it has an editorial consequence. Comparing angles rather than magnitudes means length is deliberately discounted. A short, precise passage and a long, rambling one that mean the same thing point in roughly the same direction. Writing more does not push you closer to a query. It mostly adds material that points somewhere else, which drags the average direction of the passage away from whatever you were trying to be found for.

Why there is no single embedding of your page

Because every engine uses its own model. Your page has as many vector representations as there are systems reading it, and they disagree with each other. The MTEB benchmark, covering 8 tasks across 58 datasets and 112 languages, found that no particular text embedding method dominates across all tasks.

This is the section that should change what you do, and it is missing from nearly every page on this query.

People talk about "your embedding" as though it were a property of your page, like its title tag. It is not. It is a property of a specific model at a specific version, and there are many. Google uses its own, OpenAI uses its own, each assistant and each vector database in between makes its own choice. Swap the model and the neighbourhood your page sits in changes.

The benchmark literature makes the disagreement explicit rather than theoretical. Muennighoff and colleagues built MTEB, the Massive Text Embedding Benchmark, spanning 8 embedding tasks across 58 datasets and 112 languages, and benchmarked 33 models on it. Their headline finding was that no particular text embedding method dominates across all tasks. The maintainers' own write-up on Hugging Face puts the practical version bluntly: "Model performance varies a lot depending on the task and dataset, so we recommend checking the various tabs of the leaderboard before deciding which model to use!"

Should you chunk your content?

Google says you do not need to. Its guide to optimizing for generative AI features states there is no requirement to break your content into tiny pieces for AI to better understand it, and that Google systems can understand the nuance of multiple topics on a page. Retrieval does work on passages, so write sections that each answer something. That is a writing instruction, not a chopping instruction.

A whole cottage industry has grown around chunking advice, much of it recommending that publishers pre-slice their pages into small self-contained blocks to help the machine. It is worth reading the source everyone is talking around. Google's guide to optimizing for generative AI features on Search, last updated on 10 July 2026, states that "There's no requirement to break your content into tiny pieces for AI to better understand it. Google systems are able to understand the nuance of multiple topics on a page."

The confusion is understandable, because there is a real mechanism underneath. Retrieval systems do operate on passages, and a page where the answer to a question is scattered across nine paragraphs offers no clean passage to lift. But the fix for that is to answer the question plainly in one place, which is editing. It is not to shred a good page into fragments that individually mean very little. We wrote up the mechanics of this in French, in a piece on chunking and why the manual version is unnecessary, alongside a companion on how much of a page actually gets read before grounding.

What embedding retrieval provably cannot do

It has documented mathematical limits. Work accepted at ICLR 2026 shows the number of top-k document subsets returnable by any query is bounded by the embedding's dimension, and that the best available models fail on deliberately simple tasks. The system judging your content is lossy by construction, not by accident.

Orion Weller, Michael Boratko, Iftekhar Naim and Jinhyuk Lee, in On the Theoretical Limitations of Embedding-Based Retrieval (accepted at ICLR 2026), connect results from learning theory to retrieval and show that "the number of top-k subsets of documents capable of being returned as the result of some query is limited by the dimension of the embedding". They then build a deliberately simple dataset called LIMIT and report that "even state-of-the-art models fail on this dataset despite the simple nature of the task", concluding that the single vector paradigm is fundamentally constrained.

Follow-up work sharpened the diagnosis instead of softening it. In On Strengths and Limitations of Single-Vector Embeddings (March 2026), Archish S, Mihir Agarwal, Ankit Garg, Neeraj Kayal and Kirankumar Shiragur found that dimension alone does not explain the failures. The larger contributors are domain shift and a misalignment between embedding similarities and the task's underlying notion of relevance. Two things sitting close together in the model's space is not the same as one being the right answer to the other. They also report that finetuning to fix the problem causes damage elsewhere, with performance on MSMARCO dropping by more than 40%, and that single-vector models are more vulnerable to the effect where a growing corpus drowns the right document, because the similarities function partly as noisy statistical proxies for relevance.

The strategic consequence is underrated. If similarity is a noisy proxy for relevance, and the noise worsens as the corpus grows, then publishing more near-identical pages hurts you twice. It adds to the crowding everyone is drowning in, and it splits your own meaning across several indistinct points instead of one clear one. Which is a technical justification for what good editors have said for decades.

What you can actually control

Four things, and none of them involve the vectors. Say one thing per passage. Answer the question before the reader scrolls. Make each page distinct from your own other pages. Connect them so the relationships are legible. The embedding is computed from the text, so the text is the entire interface.

There is a scale argument for why this beats a keyword-by-keyword approach, and our own numbers make it concrete. Across the 4283 French keywords Cicero Studio measured, the median volume is 260 searches/month. That is our internal data from our own keyword corpus, and we label it as ours rather than dressing it up as industry research. A median in that range means the market is not made of a handful of big queries you can each build a page for. It is made of a very long tail, most of which no keyword tool will ever show you, and which retrieval reaches by proximity rather than by exact match. Covering a subject properly is not a stylistic preference at that distribution. It is the only method that scales.

One warning, because the temptation is structural. "Cover the subject" reads as permission to publish volume, and volume is precisely what the research above suggests will hurt you as the corpus grows. Coverage means answering more real questions, not producing more pages that mean the same thing. If two of your pages would land in the same neighbourhood, you do not have two assets. You have one asset and one competitor, and you own both.

What this does not do

The honest part, which this topic needs more than most, because it is currently sold as a lever when it is mostly a description of plumbing.

What the topic does give you is a reliable filter for advice. Any recommendation that only makes sense if you could see or influence the vector directly is wrong, and you can now spot it in one reading. That is a smaller claim than most pages on this subject make, and it is one you can act on today.

Alexis Dollé

CEO & Founder of Cicero Studio

A growth specialist and content strategy consultant, I founded Cicero to help businesses build durable organic visibility, on Google as in AI answers. Day to day, I run our clients' audits and editorial production: we put AI to work for production, never in place of expertise. Every piece is built to convert, not just to exist.

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Where Cicero Studio fits

Cicero Studio does not sell embedding optimization, because it does not exist. We run the three things that do move retrieval, as one loop: a GEO audit that establishes what you are findable for today, editorial production that closes the real question gaps, and automated semantic internal linking. It starts with a free audit.

The method hook is easy to state and harder to run as a single loop: GEO audit, then editorial production, then automated semantic meshing. The audit maps which parts of a subject you actually occupy versus which you only think you occupy. Production closes the gaps with pages that answer a real question plainly, which is the only input a vector ever sees. The meshing keeps the relationships between them explicit, because well-covered material scattered across disconnected pages is a set of isolated points rather than a body of work.

We have run this method across the 1211 SEO/GEO audits produced by Cicero Studio, which is where the pattern in this article comes from: the recurring failure we see is almost never a technical one, it is several pages that mean the same thing competing for the same coordinates. Those audit and keyword figures are our own internal data, and we say so rather than presenting them as third-party research. That is what we mean by agency-quality work, software-grade productivity. The full French treatment of the model lives on our agence GEO pillar, and this page has a French sibling: vector embeddings SEO, la définition.

Going further

Vector embeddings are the plumbing. The pieces below cover what you build on top of it, starting with the discipline this page is the mechanism for. Several are in French, our home market, and are flagged as such.

Editorial transparency. This page carries no sponsored placement and no affiliate link. Every source below is cited because it is primary, and we don't cite a secondary write-up when the original is available. The audit and keyword counts are our own internal figures, and we label them as such rather than presenting them as third-party research.

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