What Is an Embedding?
An embedding is a list of numbers that represents a piece of data — a word, a sentence, an image — as a point in a high-dimensional space, placed so that similar things sit close together. A model learns the mapping, so distance becomes a measure of meaning. Embeddings are the common currency between a model and a vector database, and the reason an agent can retrieve by similarity rather than exact match.
Meaning as coordinates
A good embedding model places "invoice" and "bill" near each other and both far from "glacier", without anyone hand-coding the relationships. That geometry is what lets a system compare two things it has never seen paired before, which is the foundation of semantic search and retrieval.
For an agent, the embedding is the bridge: it turns a question into coordinates, finds the nearest stored passages, and reasons over what it retrieves — grounding an answer in a corpus far larger than its context window.
Related standards
Questions
Is an embedding the same as the model?
No. The model produces embeddings; the embedding is the output — a fixed-length vector representing one input.
Do all embeddings use the same number of dimensions?
No. Dimensionality varies by model, and vectors from different models are not directly comparable.
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By Michael Gord · published 2026-10-09 · part of the Agentic Encyclopedia. Dates are the day of publication; events are cited at their own dates.