Glossary · AI & Agents
Embeddings
Overview
What embeddings are
An embedding is a list of numbers—often hundreds or thousands of them—produced by a model that has learned to place related concepts near each other. Words, sentences, or whole documents with similar meaning get similar vectors, so "invoice" and "bill" land close together while "invoice" and "giraffe" land far apart.
The key property is that distance in this space reflects semantic similarity. That turns a fuzzy human notion—these two things mean about the same thing—into a number a computer can compute and rank.
Why embeddings matter
Embeddings are the bridge between raw content and modern AI features. They let you search by intent instead of exact keywords, group similar support tickets, recommend related products, detect duplicates, and—crucially—retrieve the right context for a language model in a RAG system. Almost every "AI understands our content" feature depends on them underneath.
Quality varies by model and domain, and embeddings must be generated with the same model on both stored data and incoming queries to be comparable. Choosing and evaluating the right embedding model is part of building a system that actually returns relevant results.
How we apply embeddings
We use embeddings across RAG development, knowledge assistants, chatbots, and customer-service AI—embedding your documents, storing them in a vector database, and retrieving the most relevant pieces at query time. In AI consulting engagements we also help teams pick embedding models and measure retrieval quality, so the features built on top stay accurate.
Where we use it
Related Zen in Tech services
How our team puts Embeddings to work in real projects.
FAQ
Embeddings — common questions
What is the difference between embeddings and a vector database?
Embeddings are the numerical vectors that represent your data; a vector database is the system that stores and searches them efficiently. You generate embeddings with a model, then store them in a vector database to enable fast similarity search.
Are embeddings only for text?
No. The same idea applies to images, audio, code, and more. Any data type can be embedded into vectors so similar items sit close together, which is how image search and multimodal AI features work.
How much do embeddings cost?
Generating embeddings through an API is typically inexpensive per item, priced by tokens or requests, and open-source models can run at no per-call cost on your own hardware. The larger costs are usually storage, retrieval infrastructure, and engineering.
Keep exploring
Related terms
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