What Is an Embedding?
An embedding is a text's meaning turned into a list of numbers, a vector, where texts with similar meaning end up close together. It lets computers compare meaning instead of exact words. Embeddings are used for semantic search, RAG, and duplicate detection, and are often stored in a vector database.
An embedding is one of those terms that sounds more complicated than it is. At its core, it’s a way of converting text into a list of numbers so the meaning comes along with it. It lets a computer compare what texts are actually about, not just which letters they contain – and it’s the engine behind modern search and RAG.
Meaning as Numbers
Picture every piece of text getting a long list of numbers attached to it, called a vector. The numbers are chosen so they capture the text’s meaning. What matters is what happens when you compare two such lists: texts that mean roughly the same thing get vectors that sit close together, while texts about entirely different things end up far apart.
An intuitive example makes it clear. The words “invoice” and “bill” end up close together, since they mean nearly the same thing. “Invoice” and “vacation” end up far apart, since they’re about entirely different things. The computer doesn’t understand the words the way a human does, but the distance between the vectors becomes a measure of how similar the meanings are.
That’s the whole basic idea: meaning translated into numbers, where closeness means similarity. You don’t need to understand how the numbers are computed to understand what they’re used for.
What Embeddings Are Used For
Once text can be compared by meaning, several uses open up. Three are especially common:
| Use | What embeddings make possible |
|---|---|
| Semantic search | Find documents by meaning – "bill" matches a text about "invoice" |
| RAG | Retrieve the right document to give a language model before it answers |
| Duplicate detection | Spot texts that mean the same thing despite different wording |
Semantic search is perhaps the clearest case. A regular search requires the user to guess the right word; a search built on embeddings finds the right result even when the document expresses the same thing in different words. That’s the difference between searching for exact letters and searching for meaning.
RAG builds directly on this. When a language model is going to answer based on your own documents, embeddings are used to find precisely the passages relevant to the question, so the model gets the right material served up. Duplicate detection, finally, lets you find entries that actually cover the same thing even if they’re worded differently – useful when cleaning up registries and knowledge bases.
The Relationship to Vector Databases
A natural follow-up question is where all these vectors end up. The answer is usually a vector database – a database built to store embeddings and quickly find the vectors closest to a given query. You create embeddings for your documents, put them in the vector database, and it then handles the similarity search itself efficiently, even when the documents number in the thousands.
For a decision-maker, it’s enough to understand the chain: text becomes embeddings, embeddings are stored in a vector database, and together they make it possible to search and reason by meaning. That’s the foundation underneath most solutions where AI works with your own texts.
How Embeddings Are Created – and an Important Caveat
The actual conversion is handled by an embedding model, a specialized AI model trained solely to translate text into vectors. You feed in a passage of text and get its vector back. It’s a fast and relatively cheap operation compared to having a large language model generate text, which is one reason semantic search scales well even for large volumes of documents.
One caveat is worth mentioning. Embeddings capture meaning remarkably well, but they don’t understand everything – irony, highly specific jargon, or nuances that require background knowledge can get lost. Quality also depends on which embedding model is used and how well it fits your language and domain. For Swedish text and industry-specific terms, it’s worth testing on real examples rather than assuming any model will do. Want to know how this could apply to you? Read more about our work with AI or get in touch.
Frequently asked questions
What is an embedding, simply explained?
It's a way of converting text into numbers so the meaning is preserved. Each text gets a list of numbers, a vector, and the nice part is that texts meaning roughly the same thing get vectors that sit close together. The computer can then compare meaning instead of just matching exact words against each other.
What's the difference from searching by keyword?
Keyword search looks for exact word matches. Embeddings capture meaning, so a search for 'invoice' can find a document talking about 'bill' even though the word invoice never appears. That makes search far more accurate when users and documents describe the same thing with different words.
What are embeddings used for?
Mainly three things: semantic search, where you find results by meaning instead of exact words; RAG, where the right document is retrieved before a language model answers; and duplicate detection, where texts that mean the same thing can be found even if the wording differs. What they share is that they all rely on comparing closeness between vectors.
How do embeddings relate to vector databases?
A vector database is built to store embeddings and quickly find the vectors closest to a given vector. Once you've created embeddings for your documents, you put them into a vector database, and it then handles the similarity search itself efficiently, even when there are many documents.
Do you need to understand the math behind it to use embeddings?
No. It's enough to understand the idea: text becomes numbers that capture meaning, and closeness between the numbers corresponds to similarity in meaning. A ready-made embedding model handles the actual calculation. As a buyer, it's enough to know what the technology does and which problems it solves, not exactly how the vectors are computed.