Quick definition
Embeddings are numerical representations of the meaning of a text, image or data item, expressed as vectors — lists of numbers — in a multi-dimensional space. Two items with similar meanings generate vectors that are mathematically close to each other, even if they use completely different words. They are the mechanism that allows an AI system to compare meaning, not just literal text matches.
What does it mean?
A traditional keyword-based search engine compares literal text: if a user searches for "waterproof parka" and the product is described as "water-resistant shell jacket", there is no word match and the search fails, even though the meaning is identical. Embeddings solve this problem by representing meaning, not exact words: a model trained to generate embeddings converts each text into a vector, and texts with similar meaning produce nearby vectors in that space, regardless of the specific vocabulary used.
This closeness is measured mathematically, typically with cosine similarity or Euclidean distance between vectors. The result is that "waterproof parka" and "water-resistant shell jacket" end up located very close to each other in the vector space, allowing a system to recognize them as equivalent without having been explicitly programmed with that rule.
Embeddings are not exclusive to text: there are embeddings for images, audio and multimodal combinations, all following the same principle of representing meaning as a position in a mathematical space.
Why it matters
Keyword search systematically fails on synonyms, typos, regional slang or ambiguous natural-language queries. Embeddings solve that problem by operating on meaning instead of literal text, which is especially critical in digital commerce, where shoppers describe products with inconsistent vocabulary and where the catalog may contain thousands of naming variants for similar concepts.
They are also the technical foundation that makes it possible for an LLM to "understand" relevant context retrieved from an external knowledge base — without embeddings, there would be no efficient mechanism to find which information is semantically relevant to a given query.
How it works
An embedding model — trained specifically for this task — receives a text and produces a fixed-length vector, for example 768 or 1536 numbers. This process is applied both to the documents or products to be made searchable (generating their embeddings in advance) and to each new query at the moment it is made.
The resulting vectors are stored in a vector database, optimized to quickly find the vectors closest to a given one, even among millions of records. When a query arrives, it is converted into a vector and compared against the database to retrieve the semantically closest items, in a process known as semantic search or vector similarity search.
Applied example in AI Commerce
A fashion marketplace generates embeddings for every product in its catalog, combining the text description and, in more advanced implementations, the product image. When a shopper types "something elegant for a daytime wedding, but not black", the system converts that query into an embedding and retrieves products whose vectors are close — light-colored, formal dresses — even though none of those products use exactly those words in their listing. This capability would not exist with a traditional keyword search engine.
Related concepts
Embeddings are the fundamental input of a Vector Database, which exists specifically to store and query them efficiently. They are the mechanism behind Semantic Search and an indispensable component of RAG, where they are used to retrieve relevant context before an LLM generates an answer. They are applied directly in AI Personalization, comparing a customer's behavior profile against the catalog.
Common mistakes
An embedding is confused with a simple text encoding (such as a hash): an embedding captures semantic meaning; it is not a reversible transformation or a lossless compression. It is also assumed that any embedding model works for any domain: models trained on general language can perform worse on catalogs with technical or specialized vocabulary, where fine-tuning or choosing domain-specific models is advisable. Finally, embeddings are believed to eliminate the need for structured metadata: in practice, the best systems combine embeddings with structured filters (price, availability, category) from the PIM.
The Edgebound Labs perspective
At the lab we do not treat embeddings as a black box you plug in and it just works. We measure the quality of an embedding model against the real vocabulary of the catalog and the shopper, not against a generic benchmark. An embedding model that is excellent in general can perform poorly on a technical B2B catalog if it never saw that vocabulary during training — verifying this is part of the method, not an optional detail.
Frequently asked questions about embeddings
Is an embedding the same as a keyword?
No. A keyword compares literal text; an embedding compares meaning, represented as a numerical vector.
Do embeddings only work with text?
No. There are also embeddings for images, audio and multimodal combinations, all representing meaning as vectors.
Can I reverse an embedding to recover the original text?
Not directly. An embedding is not a reversible compression; it is a representation optimized for comparing meaning, not for reconstructing the input.
How large is a typical embedding?
It varies by model, commonly between 384 and 1536 numbers (dimensions) per vector.
Do I need to retrain embeddings when my catalog changes?
There is no need to retrain the model; it is enough to generate new embeddings for the new or modified products and add them to the vector database.
Do embeddings replace filter-based search (price, size, category)?
No. They complement each other: embeddings capture semantic meaning, while structured filters guarantee exact constraints such as availability or price.
Keep exploring the glossary
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