Embeddings are numerical representations of data — words, sentences, images, even entire documents — expressed as an array of numbers (a vector) that a machine learning model can compare mathematically. The core idea, as IBM’s explainer on vector embeddings puts it, is that “vector embeddings are numerical representations of data points that express different types of data, including nonmathematical data such as words or images, as an array of numbers that machine learning (ML) models can process.” Once your data is a vector, a computer can do something it couldn’t do with raw text: measure how similar two things actually are.
How Does AI Turn Words Into Numbers?
An embedding model is trained on huge amounts of text (or images, or audio) and learns to place similar items close together in a high-dimensional space — often hundreds or thousands of dimensions — and dissimilar items far apart. The classic example: the vector for “king” minus “man” plus “woman” lands close to the vector for “queen,” because the model has learned a consistent numerical relationship between those concepts from how they’re used across its training data. Nothing about the number itself is meaningful on its own; what matters is distance and direction relative to other vectors.
How Are Embeddings Actually Generated?
There are two common approaches. The first is using a pretrained embedding model — options like Word2Vec, GloVe, and BERT-based embedding models are trained on massive general datasets (Wikipedia, Common Crawl, and similar) and can be applied directly to new text without retraining. The second is fine-tuning or training a custom embedding model on domain-specific data, which is common in fields like medicine or law where general-purpose vocabulary doesn’t capture the specialized meaning of terms well. Most developers today just call an embeddings API — Claude, OpenAI, and other providers all offer one — rather than training a model from scratch.
What Can You Actually Do With Embeddings?
Three use cases show up constantly. Semantic search lets a system find results based on meaning instead of exact keyword overlap — searching “fruit” can surface documents about apples and oranges even if the word “fruit” never appears in them. Recommendation systems use embeddings of products or content to find items similar to what a user already liked. And retrieval-augmented generation uses embeddings to find the most relevant chunks of a document collection to hand to a language model before it answers a question — which is exactly how our guide to retrieval-augmented generation (RAG) describes the retrieval step working under the hood.
How Is This Different From Plain Keyword Search?
A keyword search engine matches literal strings — search “car” and it won’t find a document that only says “automobile,” even though a human reader would consider them nearly the same thing. Embedding-based (semantic) search compares meaning, so it can bridge that gap. The tradeoff is that embedding search needs a vector database and an embedding step for every piece of content, which adds infrastructure keyword search doesn’t need — that’s part of why many production systems combine both, using keyword search for exact matches (product codes, names) and semantic search for everything fuzzier.
Frequently Asked Questions
Do embeddings and a model’s context window solve the same problem?
No, they solve different problems. A context window is how much text a model can consider at once when generating a response. Embeddings are how you find the *right* small slice of a much larger dataset to feed into that context window in the first place, so you’re not stuck trying to cram an entire document library into a single prompt.
Are embeddings only used for text?
No. Images, audio, and even user behavior data can all be turned into embeddings, and some models generate multimodal embeddings that place, say, a photo of a dog and the word “dog” close together in the same vector space.
Do I need to understand the math to use embeddings?
Not for most practical use cases. Vector databases and embedding APIs handle the distance calculations for you — you mainly need to understand what embeddings are good at (finding similar meaning) and where a vector database fits in your architecture, rather than the underlying linear algebra.
For a deeper technical walkthrough of how vectors are generated and compared, see IBM’s guide to vector embeddings, and for the search-specific angle, Pinecone’s introduction to vector embeddings is a solid next read.
