Definition
An embedding is a list of numbers that represents the meaning of a piece of content, such as a word, a sentence, a document, an image or a product. The list is called a vector. An AI model produces it in such a way that items with similar meanings get similar numbers.
That property is what makes embeddings useful. A computer can’t compare the meaning of “running shoes” and “jogging sneakers” directly, since the two phrases share no words. Once both are converted to embeddings, their vectors sit close together, and the closeness can be measured with simple arithmetic.
The idea became widely known through Word2Vec, a method published in 2013 by Tomas Mikolov and colleagues at Google. Their paper gave the example that’s still used to explain the concept. Taking the vector for “King,” subtracting the vector for “Man” and adding the vector for “Woman” gives a result closest to the vector for “Queen.” Relationships between meanings showed up as consistent directions in the numbers.
Embeddings are also called vector embeddings or vector representations. The system used to store and search them is a vector database.
See also: Retrieval-Augmented Generation, Large Language Models (LLM), Natural Language Processing (NLP), LLM Tokens
Why it matters for marketing
Embeddings sit underneath a lot of the AI features marketers already use, usually without being named.
Site search that understands a shopper typing “something warm for hiking” and returns fleece jackets is matching on embeddings, not keywords. Product and content recommendations often work by finding items whose embeddings are near the ones a customer has viewed. A chatbot that answers from a company’s help center first uses embeddings to find the relevant articles. Tools that group thousands of survey comments or reviews into themes do it by clustering embeddings.
They also explain a shift in how content gets found. Traditional search matched the words on a page to the words in a query. AI search tools and assistants retrieve content by meaning, which is why generative engine optimization puts more weight on covering a topic clearly than on repeating exact phrases.
For a marketing leader, the practical value is in vendor conversations. When a platform says it offers “semantic search,” “AI-powered recommendations” or a chatbot “grounded in your content,” embeddings are almost certainly involved. Knowing that leads to better questions about what content gets embedded, how often it’s refreshed and where the vectors are stored.
How it works
- An embedding model reads the content. This is a specialized AI model, separate from the chat model that writes responses.
- The model outputs a vector. It’s a fixed-length list of numbers, typically several hundred to a few thousand of them. Each number is called a dimension.
- The vector is stored. Usually this is in a vector database, alongside a reference to the original content.
- A query gets the same treatment. When someone searches or asks a question, the query is converted to a vector by the same model.
- The system finds the nearest vectors. It compares the query vector to the stored ones and returns the closest matches.
The individual numbers don’t correspond to labeled features a person could read. Meaning is spread across the whole vector, and only the relationships between vectors are interpretable.
Types of embeddings
| Type | What it represents | Example use |
|---|---|---|
| Word embeddings | Single words | Early language models such as Word2Vec |
| Sentence and document embeddings | Longer passages of text | Semantic search, retrieval for chatbots |
| Image embeddings | Pictures | Visual search, finding similar product photos |
| Multimodal embeddings | Text and images in one shared space | Searching a photo library with a text description |
| Product or user embeddings | Items or people, based on behavior | Recommendations, lookalike audiences |
Early word embeddings gave each word one fixed vector, so “bank” had the same representation in “river bank” and “bank account.” Current models produce contextual embeddings, where the vector depends on the surrounding text.
How to calculate similarity
The most common measure is cosine similarity, which compares the direction of two vectors.
Cosine similarity = (A · B) ÷ (‖A‖ × ‖B‖)
A · B is the dot product: multiply the matching numbers in each vector and add up the results. ‖A‖ is the vector’s length: the square root of the sum of its squared numbers. The result runs from −1 to 1. A value near 1 means the two items are very similar in meaning. A value near 0 means they’re unrelated.
A simplified example using two dimensions instead of hundreds:
| Item | Vector |
|---|---|
| A: “running shoes” | (3, 4) |
| B: “jogging sneakers” | (4, 3) |
| C: “tax software” | (−4, 3) |
For A and B, the dot product is (3 × 4) + (4 × 3) = 24. Each vector has a length of 5, since √(9 + 16) = 5. Cosine similarity is 24 ÷ (5 × 5) = 0.96, so the two are very similar.
For A and C, the dot product is (3 × −4) + (4 × 3) = 0. Cosine similarity is 0 ÷ 25 = 0, so they’re unrelated.
The vectors and phrases here are invented to show the arithmetic. Real embeddings have far more dimensions, and software does the calculation.
How to utilize embeddings
Semantic search. Return results that match what a customer means, even when the wording differs from the product catalog.
Recommendations. Suggest products or articles similar to ones a customer has engaged with.
Chatbots and assistants grounded in company content. Retrieve relevant passages from help articles, policies or product data so the AI answers from them. This is the retrieval step in retrieval-augmented generation.
Clustering feedback. Group open-ended survey responses, reviews or support tickets into themes without reading each one.
Content audits. Find near-duplicate pages or gaps in topic coverage across a large site.
Audience and lookalike modeling. Represent customers as vectors based on behavior and find others who resemble the best ones.
Classification. Sort incoming messages by intent or topic.
Personalization. Match content to a visitor’s inferred interests in real time.
Comparison with related concepts
| Term | What it is | Relationship to embeddings |
|---|---|---|
| Embedding | A vector representing the meaning of content | The subject of this entry |
| LLM Tokens | The pieces a model splits text into | The input. Text is tokenized first, then represented as vectors |
| Vector database | A system for storing and searching vectors | Where embeddings are kept |
| Keyword search | Matching the exact words in a query | The older approach that semantic search complements |
| Semantic search | Search that matches on meaning | An application built on embeddings |
| Retrieval-Augmented Generation | Retrieving content and giving it to a model to answer from | Usually relies on embeddings to find the content |
| Fine-tuning | Further training that changes a model’s weights | A different way to customize AI. Embeddings leave the model unchanged |
| Knowledge graph | A structured map of entities and their relationships | Explicit, human-readable relationships. Embeddings capture them implicitly |
Tokens and embeddings are often confused. A token is a unit of text. An embedding is a set of numbers that represents meaning. Tokens are how text is fed in, and embeddings are how the model represents it.
Keyword search and embedding-based search have different strengths. Keyword search is reliable for exact terms such as a product code or a brand name. Embedding-based search handles paraphrase and intent. Many systems combine the two, an approach called hybrid search.
Best practices
Use the same model for content and queries. Vectors from different embedding models aren’t comparable.
Break long content into sensible chunks. A whole 40-page guide embedded as one vector loses detail. Splitting by section gives better matches.
Keep embeddings in sync with content. When a product page or policy changes, its embedding needs to be regenerated.
Clean the source content first. Outdated or contradictory pages will be retrieved along with good ones.
Combine with keyword search where precision matters. Exact part numbers, names and codes are better handled by keyword matching.
Test with real queries. Check what the system returns for the questions customers actually ask.
Treat embeddings as sensitive data. Vectors built from customer records or confidential documents carry information about them and need the same access controls.
Watch for bias. Embeddings learn associations from their training data, including stereotypes, and those can carry into search results and recommendations.
Plan for model changes. Switching embedding models means re-embedding all the content.
Future trends
Multimodal embeddings. Models that place text, images, audio and video in one shared space are making it possible to search across formats with a single query.
Built into existing databases. Vector search has been added to mainstream databases and data platforms, so a separate specialized system is less often required.
Embeddings for AI agents. Agents use embeddings to look up relevant tools, documents and past interactions as they work.
Content discovery by meaning. As AI search and assistants handle more queries, how a brand’s content is represented in embedding space affects whether it gets retrieved.
Adjustable vector sizes. Newer embedding models let users shorten vectors to cut storage cost with a limited loss of accuracy.
Scrutiny of bias and privacy. Research continues on how much of the original content can be recovered from an embedding and how to reduce learned bias.
Frequently asked questions
What is an embedding in AI? It’s a list of numbers that represents the meaning of a piece of content, arranged so that similar content has similar numbers.
What’s an embedding used for? Finding things by meaning. Common uses are semantic search, recommendations, grouping similar content and retrieving information for AI chatbots.
What’s the difference between an embedding and a token? A token is a chunk of text a model reads. An embedding is a numerical representation of meaning. Text is split into tokens, and tokens or passages are then represented as embeddings.
What is a vector database? A database built to store embeddings and quickly find the ones most similar to a given query.
What is cosine similarity? A measure of how closely two vectors point in the same direction. It’s the most common way to compare embeddings, and a higher value means more similar meaning.
How many numbers are in an embedding? It depends on the model. Several hundred to a few thousand is typical for text embeddings.
Are embeddings the same as a large language model? No. Embedding models turn content into vectors. Large language models generate text. They’re often used together.
How do embeddings relate to RAG? In retrieval-augmented generation, embeddings are typically used to find the passages most relevant to a question, which are then given to a language model to write the answer.
Do embeddings contain my actual data? They’re numerical representations, not the original text. Research has shown that some information can be recovered from them, so they should be protected like the source data.
Does a marketer need to create embeddings directly? Usually not. They’re generated automatically inside search tools, recommendation engines, chatbots and analytics platforms. Understanding them helps in evaluating those tools.
Related terms
- Retrieval-Augmented Generation
- Large Language Models (LLM)
- LLM Tokens
- Natural Language Processing (NLP)
- Generative Engine Optimization (GEO)
- Answer Engine Optimization (AEO)
- Multimodal AI
- Artificial Neural Network (ANN)
- Algorithmic Bias
- Omnichannel Personalization
Sources
- Mikolov, Tomas, Kai Chen, Greg Corrado, and Jeffrey Dean. “Efficient Estimation of Word Representations in Vector Space.” arXiv, 2013. https://arxiv.org/pdf/1301.3781
- Zilliz. “How are embeddings stored in vector databases?” https://zilliz.com/ai-faq/how-are-embeddings-stored-in-vector-databases
- Cloudian. “Vector Database: How It Works, Use Cases & Top 6 in 2026.” https://cloudian.com/guides/ai-infrastructure/vector-database-how-it-works-use-cases-top-6-in-2026.md
- AI Wiki. “Embeddings.” https://www.aiwiki.ai/wiki/embedding