Are embeddings and keywords the same thing?

No. A keyword is the literal text on the page, while an embedding is the numerical meaning the model reads from that text. You can rank for a query with no exact keyword match if your meaning is close enough.

How many dimensions does an embedding usually have?

It depends on the model, but common sizes range from a few hundred to a few thousand values. More dimensions can hold finer detail, though the practical takeaway for content is the same either way.

Can I actually see my content's embeddings?

Yes. Embedding models are available through APIs and open tools, so you can generate a vector for your page and compare it against a query vector to check how close they sit.

Do embeddings replace links and authority signals?

No. Embeddings handle the meaning match that decides whether your page is relevant, but trust, links, and site quality still shape which relevant pages win. Both layers matter.

Which embedding model does Google use?

Google uses its own internal systems rather than a public one, including the RankEmbed model behind Neural Matching, alongside BERT and MUM for deeper context. You cannot target a specific model, so you optimize the meaning instead.

How is optimizing for embeddings different from vector search SEO?

They describe the same underlying idea from two angles. Embeddings are the numerical representations of meaning, while vector search SEO is the wider practice of shaping content so that those representations match the right queries.

What Are Embeddings, and Why Should an SEO Care | Klara Pelhe

What Are Embeddings, and Why Should an SEO Care?

Embeddings are how modern search engines and AI assistants read what your content is about. They turn your words into numbers that capture meaning, then use those numbers to decide whether your page answers a query. This article explains what embeddings are, how they work, why they matter for SEO today, how to optimize your content for them, and the mistakes that quietly cost you visibility.

TL;DR

Embeddings are numerical representations of text that capture meaning, not just the words on the page. A model reads your content and outputs a vector, which is a long list of numbers that places the meaning in a shared space.
Texts with similar meaning land close together. "Car" and "automobile" sit near each other even though they share no letters, so the engine can connect them.
Google already runs on this. RankBrain used embeddings for query understanding in 2015, and Neural Matching added embedding-based retrieval through a system Google calls RankEmbed in 2018.
AI search depends on them, too. ChatGPT, Perplexity, and AI Overviews retrieve passages based on vector similarity, then write an answer from the most similar ones.
Closeness is measured with cosine similarity, which scores how near two vectors sit in that shared space.
You optimize by writing clear, focused content on a single concept, so the model produces a clean vector that aligns with the queries you want.
Keyword stuffing now works against you, since repetition blurs the meaning the model is trying to capture.

What Are Embeddings?

An embedding is a list of numbers that represents the meaning of a piece of text in a form a machine can compare. Models trained on large amounts of text learn to represent words, sentences, and full passages as points in a high-dimensional space, where items with related meaning sit close together and unrelated ones sit far apart.

The key shift is that the numbers stand for meaning, not spelling. A search engine no longer needs the exact phrase to find a match, because "how to fix a flat tire" and "repairing a punctured wheel" produce vectors that are close to each other. This is the format that semantic search, Google AI Overviews, and AI assistants all rely on underneath.

What Are Embeddings, and Why Should an SEO Care?

TL;DR

What Are Embeddings?

FAQ

How Do Embeddings Function

Why Vector Embeddings Matter in SEO Right Now?

How to Optimize Content for Embeddings

Common Mistakes to Avoid When Optimizing Content for Embeddings