- An embedding is a list of numbers that captures meaning
- The map picture of an embedding
- Why embeddings help AI handle raw data
- Kinds of embedding for words, sentences, pictures and more
- What embeddings are used for in everyday tools
- Embeddings next to vector databases and semantic search
- Where embeddings fall short
- Making use of embeddings without building them
- Frequently asked questions
An embedding is a list of numbers that an AI model creates to represent the meaning of a word, sentence, image or product, so that similar things end up with similar numbers. The part people trip over: the numbers aren't a code anyone reads. Their value is that closeness stands for similarity.
Embeddings sit underneath most modern AI, yet almost nobody explains them in normal language. I think that's a shame, because the idea is lovely and simple once you see it. If you've ever had an app suggest "more like this", you've benefited from one.
An embedding is a list of numbers that captures meaning
AWS describes embeddings as numerical representations of real-world objects, which AI systems create automatically during training. IBM goes a step further: embeddings represent objects such as text, images and audio as points in a continuous vector space, and objects with similar meaning sit closer together.
So an embedding is what you get when an AI model reads a word, a sentence, a photo or a product and answers with a long list of numbers. The list is like a set of coordinates. Two things that mean similar things get similar coordinates.
The map picture of an embedding
Think of a huge map where every sentence in your business has a pin. "Where is my order?" and "My parcel hasn't arrived" land almost on top of each other, because they mean nearly the same. "Can I change my email address?" sits in a different neighbourhood entirely. Nobody told the model about parcels or email addresses. It placed the pins by learning from huge amounts of examples.
IBM notes that these lists can reach a thousand dimensions or more. You can't picture that, and you don't need to. The only thing that matters is the rule of thumb: close together means similar in meaning.
Why embeddings help AI handle raw data
Computers can't compare meaning directly. AWS explains that embeddings reduce the number of dimensions by identifying commonalities and patterns between features, which lowers the computing needed to process raw data. A photo, for instance, has a separate value for every pixel colour. An embedding boils that down to the essentials.
AWS also compares embeddings with the cruder method of one-hot encoding, where every category gets its own isolated slot. In that scheme the fruits apple and orange show no similarity to each other at all, and the table gets sparse and heavy as categories multiply. Embeddings let apple and orange sit near each other, and far from a spanner.
Kinds of embedding for words, sentences, pictures and more
IBM lists several kinds. They all follow the same idea but start from different material:
| Kind | Starts from | Small business example |
|---|---|---|
| Word | Single words | Knowing "couch" and "sofa" are close |
| Sentence or document | Longer text | Matching a question to a help article |
| Image | Pictures | Finding product photos that look alike |
| Audio | Sound | Matching voice clips or music |
| Graph | Networks of links | Suggesting who is connected to whom |
Context matters. AWS gives a neat example: BERT produces different embeddings for "play" in "I went to a play" and "I like to play", whereas an older method, Word2Vec, can't tell those uses apart. Newer embeddings therefore understand a word in its sentence, not just on its own.
What embeddings are used for in everyday tools
IBM lists question answering, image retrieval and recommender systems among the uses. In daily life you meet them in:
- "Customers also liked" suggestions in an online shop.
- Search that understands a question instead of matching its exact words.
- Chatbots that find the right section of your documents before answering.
- Photo apps that find pictures similar to one you choose.
- Tools that group similar customer comments into themes.
They're also the raw ingredient for the AI answers described in my explainer on where ChatGPT gets its information, where text is turned into numbers before a model can use it.
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Embeddings next to vector databases and semantic search
These three terms travel together and people swap them around, so here's the tidy split. The embedding is the ingredient: the list of numbers describing one item. A vector database is the cupboard where lots of those lists are kept and searched. Semantic search is one dish you can cook with them, a search box that understands what people mean.
You can have embeddings without the other two. A tool might use them just to group similar customer comments into themes, with no search box and no special database in sight. Keeping the roles apart makes vendor claims much easier to read.
Where embeddings fall short
An embedding is only as good as the model and the data behind it. A model trained mostly on general web text may place specialist terms in your industry oddly. Two items that are close in the number space can still be wrong for the task, for example a refund request and a refund policy question. And embeddings reflect patterns in their training data, including any bias in it.
So test with your own examples, keep a person checking important results and read a tool's privacy page before sending it customer text to be turned into embeddings.
Making use of embeddings without building them
You rarely create embeddings yourself. They're built into the search, recommendation and chat tools you already use, and AI platforms from the big cloud providers sell embedding models for developers. If you wonder whether a project in your business needs this level of detail, an AI consultant can help you decide before you spend time in the wrong place.
Frequently asked questions
What is an embedding in AI?
It's a list of numbers that represents the meaning of something, such as a word, sentence, image or product, so that items with similar meaning end up with similar numbers.
What is an example of an embedding?
The sentences "Where is my order?" and "My parcel hasn't arrived" would get very similar embeddings, while "Change my email address" would get a quite different one.
How are embeddings created?
AWS says AI systems create them automatically during training, usually with neural networks that learn patterns from large amounts of example data.
What is the difference between an embedding and a vector?
A vector is simply a list of numbers. An embedding is a vector produced by an AI model so that its position reflects meaning.
What are embeddings used for?
IBM lists question answering, image retrieval and recommender systems. They also power search that understands meaning and chatbots that look things up in your documents.
