What are vector embeddings? Definition and how they work

Vector embeddings are lists of numbers that represent the meaning of a piece of content, such as a word, a sentence, a passage or an image, so that items with similar meaning sit close to each other in a mathematical space and a computer can compare them by distance.

Embeddings are how AI systems find content that means the same thing as a question, even when it uses different words. They sit underneath semantic search and the retrieval step of AI search engines.

How vector embeddings work

An embedding model reads a piece of text and outputs a vector: a fixed-length list of numbers, usually hundreds or thousands long. No single number means anything a person could name, but together they place the text at a point in space. Texts about the same thing land near each other; unrelated texts land far apart. Closeness is usually measured with cosine similarity, which compares the direction of two vectors.

A simple example: “cheap running shoes” and “affordable trainers for jogging” share no words, yet their embeddings are close. “Running shoes” and “running a business” share a word, yet their embeddings are far apart. Classic keyword matching gets both cases wrong; embeddings get both right.

Embeddings, semantic search and RAG

Embeddings power semantic search and the retrieval step of retrieval-augmented generation. A typical pipeline works like this:

1. Documents are split into passages, a step known as chunking.
2. Each passage is turned into an embedding and stored in a vector index.
3. When a question arrives, it is embedded with the same model.
4. The system retrieves the passages whose vectors are closest to the question’s.
5. Those passages go to a large language model, which writes the answer from them.

Many systems run keyword search alongside vector search and merge the results, so the two approaches complement each other rather than compete.

What embeddings mean for your content and brand

• Meaning beats exact phrasing. A page does not need to repeat the user’s exact words to be retrieved, but it does need to cover the concept clearly.
• Passages compete, not pages. A section that mixes three topics produces a blurred vector that sits close to none of the questions. Focused sections match better. See content chunking.
• Your category has to be in the text. If your pages never say in words what you are and who you serve, their passages will not sit near the questions people ask about that category, and neither will your brand.
• Third-party passages count too. A comparison that describes you clearly is a passage that can be retrieved for category questions, with your name in it.

How to write content that embeds well

• Give each section one idea and a heading that says what it is about.
• Name the entity and its category explicitly (“Acme is a project management tool for architects”) rather than “our platform”.
• Use the words your customers use, as well as your own product terms.
• Avoid starting sections with “this” or “it”, since a passage may be read without the one before it.
• Keep key facts in HTML text, not only in images, PDFs or scripts.

Common mistakes

• Keyword stuffing. Repeating a phrase does not move a vector closer to a question; it dilutes the passage.
• Trying to optimize a vector directly. Each engine uses its own embedding model, and you cannot see it. Write for clarity, then measure results.
• Ignoring the question side. Users phrase the same need in many ways. Your content should answer the need, not one wording of it.

How to measure the effect of embeddings

You cannot observe the embeddings inside ChatGPT, Perplexity or Google, but you can observe their outcome: whether AI answers name your brand and cite your pages for the questions your customers ask, phrased in several ways. That outcome, not the embeddings themselves, is what Mencoro measures: it tracks the prompts you choose across engines and reports how often you are named and linked. Start with the AI visibility feature or a free sample from the AI visibility checker, and read our guide to AI search optimization for the wider strategy.

Related glossary terms

• Semantic search: search that matches meaning, built on embeddings.
• Retrieval-augmented generation (RAG): how AI engines retrieve passages before answering.
• Content chunking: splitting content into passages that embed and retrieve well.
• Large language model (LLM): the model that writes the answer from the retrieved passages.