Vector Embeddings for SEOs: What 'Semantic Match' Really Means in AEO

Vector embeddings are the math behind every 'semantic match' claim in AEO. When ChatGPT or Perplexity decides which of a thousand candidate passages answers a prompt, it converts both the prompt and the passages into high-dimensional number arrays and ranks by cosine similarity. The pages that win citations are the ones whose embeddings sit closest to the prompt embedding — and that distance is something you can engineer.

Table of contents

1. What is a vector embedding, in plain English? · 2. How do answer engines use embeddings to pick citations? · 3. Why does my well-ranked page never get cited? · 4. How do I write for higher embedding similarity? · 5. The role of named entities in embedding distance · 6. Tools and workflows · 7. FAQ

What is a vector embedding, in plain English?

A vector embedding is a list of 700–3,000 numbers that represents the meaning of a chunk of text. Texts with similar meaning produce vectors that point in similar directions; texts about unrelated topics point in different directions. Search and AI engines use embeddings to compare meaning instead of matching exact words.

How do answer engines use embeddings to pick citations?

After retrieving candidate URLs from a search index, the engine chunks each page into passages, embeds every passage with a smaller, fast model (OpenAI's text-embedding-3, Google's Gecko, Cohere's embed-v3 are common), and embeds the user's prompt the same way. It then computes cosine similarity between the prompt vector and every passage vector and keeps the top 3–8 to ground the answer.

Why does my well-ranked page never get cited?

Because Google ranking and embedding similarity are two different scoring systems. A page can rank #1 on the keyword 'best CRM' because it has 200 backlinks and clean technical SEO, yet still lose the citation race to a #14 page whose passages embed closer to the prompt 'what's the best CRM for a 5-person SaaS team that already uses HubSpot for marketing'. **The blue-link engine ranks pages; the answer engine ranks passages — and they're not the same skill.**

How do I write for higher embedding similarity?

Three practical moves. (1) Mirror the prompt's full noun phrases in your H2 and first sentence — 'best CRM for a 5-person SaaS team' should appear verbatim somewhere. (2) Co-occur related entities the prompt implies (HubSpot, pipeline, deal stages, owner assignment) so the embedding picks up the full topical context. (3) Avoid burying the answer under throat-clearing — the first 50 words of the passage carry disproportionate weight in the chunk's embedding. Anthropic and Cohere both publish embedding documentation that confirm this front-loading effect.

The role of named entities in embedding distance

Named entities act like coordinates — they pull the embedding toward a specific neighborhood of vector space. A passage that names 'HubSpot, Pipedrive, Attio, Folk' will embed close to prompts about CRM comparison even if the prose is otherwise generic. **Generic adjectives ('powerful, intuitive') push embeddings toward the dense, low-signal center of the space where nothing wins.**

Tools and workflows

You don't need a data-science stack to act on this. (1) Use ChatGPT or Claude to score draft passages against your target prompt — ask the model directly which is closer. (2) Use OpenAI's embeddings API in a 20-line Python script to compute cosine similarity between your existing pages and a list of priority prompts; rewrite the laggards. (3) For ongoing measurement, layer Profound or AthenaHQ on top to track real citation outcomes.

Frequently Asked Questions

Do I need to learn machine learning to do AEO well?

No. You need a working mental model of how embedding similarity ranks passages — that's what this article gives you. The actual code is 20 lines if you ever want to run the math yourself, but most consultants get by on intuition plus one of the prompt-tracking tools.

Which embedding model do answer engines actually use?

It varies and they don't fully disclose. ChatGPT Search uses OpenAI's own embedding stack, Perplexity uses a hybrid, Google AI Overviews uses Gecko-family models. The good news: optimizing for one usually transfers because they're all trained on similar web corpora.

Can I 'stuff' entities to game the embedding?

No. Modern embedding models penalize unnatural repetition and reward genuine topical co-occurrence. Stuff and you'll move toward the spammy region of vector space, not the authoritative one.

Are short pages or long pages better for embedding similarity?

Neither — passages are. Engines chunk every page into 200–500 token windows and rank each chunk independently. A 3,000-word page can win citations on six different prompts if each section is structured as a clean self-contained chunk.

How often do I need to re-check my embedding fit?

Quarterly is enough for most niches. Recheck immediately after any major model update from OpenAI, Anthropic or Google — the underlying embedding spaces shift and so does what wins.