What is the difference between embeddings and vector embeddings?

There's no practical difference. An embedding is stored as a vector, which is just an ordered list of numbers, so people use the two terms interchangeably.

How are embeddings created?

An embedding model reads the input and outputs a fixed-length list of numbers. The model is trained on large amounts of text, and during training it learns to place related meanings close together, so the numbers end up reflecting meaning more than exact wording.

What are word embeddings?

Word embeddings give each individual word its own list of numbers, an approach made popular by Google's word2vec research in 2013. Newer models create embeddings for whole sentences and passages, so the same word can carry a different meaning depending on the words around it, and that passage-level matching is what AI search retrieval relies on.

What is the difference between an embedding model and an LLM?

An embedding model turns content into numbers for comparison and search, while a large language model generates text. Many AI search systems use both, with embeddings finding the relevant passages and the LLM writing the answer from them.

Do marketers need to create embeddings?

No. AI engines create their own embeddings of the content they retrieve. What you control is the content itself, and clear passages that state one idea well are easier for any retrieval system to match to a question.

What Are Embeddings? A Plain-English Guide for Marketers

Definition

EmbeddingsEmbeddings are lists of numbers that represent the meaning of a piece of content, such as a paragraph of text or an image. Content with similar meaning gets similar numbers, so a system can find a relevant passage even when it shares no words with the question. Embeddings are what make semantic search and AI retrieval work.

How embeddings work

An embedding model reads a piece of content and turns it into a long list of numbers. The lists are long: OpenAI's text-embedding-3-small model, for example, returns 1,536 numbers for every input. Each list works like a set of coordinates, so content with similar meaning lands close together and unrelated content lands far apart.

It helps to picture a map. On that map, "affordable running shoes" sits close to "cheap sneakers for jogging," even though the two phrases share almost no words, while "running a small business" sits somewhere else entirely despite sharing the word "running." The embedding captures what the phrase means, which is why the letters matter so little.

To find content that answers a question, a system converts the question into an embedding, compares it with the embeddings of stored passages, and returns the passages that sit closest. That comparison is called vector search. It stays fast even across millions of passages, which is why it underpins so many search and recommendation systems. The embeddings themselves are usually stored in a vector database built for this kind of lookup.

Embeddings vs keywords

Traditional keyword search matches the words in a query with the words on a page. It works well when people use the same vocabulary as the page and poorly when they don't.

Embeddings match on meaning, which is the basis of semantic search. A question like "how do I stop ChatGPT describing my brand wrong" can match a passage about correcting inaccurate AI answers, even without any shared phrasing.

Both approaches have blind spots. Embeddings can miss exact matches such as product codes or rare names, and keyword search misses paraphrases, so many retrieval systems combine the two in what's called hybrid search.

Why embeddings matter for AI search

When an AI engine searches the web to answer a question, it has to decide which passages are worth reading. Systems built on retrieval-augmented generation commonly use embeddings for that step. They retrieve the passages closest in meaning to the question and hand them to a language model, which writes the answer and cites its sources.

For brands, that plays out in a few practical ways:

Passages compete with other passages. Retrieval systems usually split pages into smaller sections before creating embeddings, a process called content chunking. A focused passage that answers one question has a better chance of matching it than a paragraph that mixes several ideas.
Meaning counts for more than wording. A passage doesn't need to repeat someone's exact phrase to match. It does need to state the idea plainly, in terms a buyer would recognize.
Context has to be local. A passage that only makes sense after the three paragraphs above it loses that context once it's separated from them. Name the subject in each section instead of leaning on "it" or "this" from earlier in the page.

Examples of embeddings in everyday tools

People use embeddings every day without noticing. A site search that returns "return policy" results for "can I send this back" is matching meaning. "Similar products" and "related articles" features often work by comparing the embeddings of items, and duplicate-detection tools compare embeddings to flag near-identical content even when the wording differs.

AI assistants lean on them too. When ChatGPT or Perplexity pulls in web sources, retrieval steps like the ones above help decide which passages reach the model.

How to see which passages AI engines pick

You can't inspect the embeddings an AI engine creates, but you can see the result, which is the set of pages it cites when it answers questions in your category. Searchable's Sources view shows the domains and URLs AI engines cite across your tracked prompts, along with the gaps where competitors get cited and you don't. Comparing those cited passages with your own pages is a practical way to find where your content isn't matching the questions buyers ask.

Frequently asked questions

Embeddings are numerical representations of content that capture what it means. An embedding model converts a sentence or an image into a long list of numbers, and items with similar meanings end up with similar lists, which AI systems compare to find related content.

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Semantic searchSemantic search is a way of finding information by matching the meaning of a query instead of its exact words. It lets a search engine or AI assistant return a page about "affordable CRM for startups" when someone asks for a "cheap customer database for a new company," even though the two phrases share almost no words.Vector searchVector search finds content by meaning instead of exact words. It turns text into embeddings, lists of numbers that represent what the text means, then returns the stored items whose embeddings sit closest to the query's. That's how a search for "cheap flights" can find a page about "budget airfare."Retrieval-augmented generation (RAG)Retrieval-augmented generation (RAG) is a technique where an AI model first searches a set of documents for passages that match a question, then writes its answer from those passages. Because it looks things up at answer time, the model can use up-to-date information and point to where it came from.Content chunkingContent chunking is the way AI search systems split a web page into smaller passages so each one can be retrieved and quoted on its own. The term also describes writing pages in clear, self-contained sections, so any passage an engine pulls out still makes sense without the rest of the page.Hybrid searchRetrieval that combines keyword matching with vector search, so results match both the exact terms and the meaning of a query.

Large language model (LLM)An AI model trained on large amounts of text to predict and generate language. LLMs are the engines inside ChatGPT, Claude, Gemini, and most AI search tools.

Browse the full AI search glossary