What Are Embeddings in AI?

How embeddings work

An embedding model reads a piece of content, such as a sentence, paragraph, product description, or (with an image or multimodal model) a picture, and outputs a fixed-length list of numbers. Depending on the model, that list is often hundreds or thousands of numbers long. Each list is a vector, and you can think of it as a set of coordinates.

A helpful analogy is a map of meaning. On a city map, two coordinates pin down any location, and places that are close on the map are close in real life. An embedding works the same way, except it uses far more dimensions than two, and “nearby” means “similar in meaning.” “Refund my order” and “I want my money back” usually land close together even though they share almost no words. “Refund my order” and “order a pizza” share a word but typically land farther apart.

Software uses straightforward math to measure how close two embeddings are. One common method, cosine similarity, checks whether two vectors point in roughly the same direction. The individual numbers are not human-readable labels; the meaning lives in the overall position. Embeddings are also different from tokens. Tokens are the small chunks of text a language model reads. The model turns each token into an internal embedding, but the embeddings businesses work with usually represent a whole sentence, passage, document, or image.

What embeddings make possible

Because embeddings turn similarity of meaning into something a computer can measure, they sit behind many everyday AI features:

• Search by meaning. A search tool can match a question to relevant content even when the wording differs. Embeddings are one of the main building blocks of semantic search.
• Retrieval for AI assistants. In retrieval-augmented generation (RAG), embeddings help find the passages in your documents most similar to a question so an AI model can answer using them.
• Recommendations. Products, articles, or help content whose embeddings sit close to something a person viewed can be suggested as related.
• Clustering and deduplication. Grouping nearby embeddings can reveal themes in feedback or tickets and flag near-duplicate records.
• Classification. Comparing a new item’s embedding with labeled examples can help sort it into a category, such as routing an email to the right team.

In each case, the embedding does not produce an answer by itself. It supplies a consistent way to measure similarity that other parts of a system build on.

Embeddings and vector databases

Creating embeddings is only the first step. Once you have a large collection, you need a place to keep them and a fast way to find the closest matches to a new query. A vector database is built for this job: it stores vectors and searches them by similarity. Many general-purpose databases and search engines also offer vector search, so a separate system is not always required, and very small collections can simply be compared directly.

A simple way to keep the pieces straight:

• Embeddings are the data: the numeric representation of each item.
• A vector database (or a database with vector search) stores that data and finds near neighbors quickly.
• Semantic search is one experience those pieces can power for the person searching.

One practical rule: embeddings from different models are not compatible. A query’s embedding should only be compared with embeddings made by the same model, version, and settings (such as output size). If you switch embedding models or versions, plan to re-embed your existing content, such as the articles in a knowledge base.

Where embeddings fall short

Embeddings measure similarity, not accuracy. Two passages can sit close together while one is outdated or wrong, so the content you embed still needs to be current and trustworthy.

• Same topic is not the same meaning. Embeddings can blur important details. “How do I cancel my plan?” and “How do I keep my plan?” may land near each other because they cover the same topic. Opposite meanings, negation (“can” vs. “can’t”), and small wording changes are common weak spots.
• Exact terms can slip. Part numbers, order IDs, and names are often better matched by keyword search, which is why many search systems use both.
• Quality depends on your choices. The model you pick, the languages it handles well, and how you split long documents into chunks all affect results. Test with real examples from your own work.
• They are still your data. Embeddings are derived from the original content, so apply the same privacy and access controls you use for the source material.

Further reading

• AI ImplementationFor teams thinking about putting AI to work in their own systems, this page describes how SN3 redesigns processes, connects systems, automates work, and deploys AI.