---
schema: 1
type: term
title: _embeddings_
description: >-
  **_Embeddings_** are numerical representations of text (or other content) as
  vectors of numbers, arranged so that items with similar meaning are
  represented by similar values. They allow software to compare and retrieve
  content by _meaning_ rather than by exact wording.
canonical: 'https://liquidgold.cc/channel/everyinc/explore/term/embeddings-2c11cbf59917'
markdown: 'https://liquidgold.cc/channel/everyinc/explore/term/embeddings-2c11cbf59917.md'
llms: null
parent: 'https://liquidgold.cc/channel/everyinc/llms.txt'
channel: everyinc
updated: '2026-10-23T22:12:35.346Z'
language: null
children: []
---


# \_embeddings\_

Canonical: https://liquidgold.cc/channel/everyinc/explore/term/embeddings-2c11cbf59917

Parent: [Channel index](https://liquidgold.cc/channel/everyinc/llms.txt)

## Definition

**_Embeddings_** are numerical representations of text (or other content) as vectors of numbers, arranged so that items with similar meaning are represented by similar values. They allow software to compare and retrieve content by _meaning_ rather than by exact wording.

Across the material in this channel — from conversations about how language models work to discussions of building AI products at scale — **_embeddings_** come up as the mechanism that lets software work with *meaning* rather than just raw text. When the channel's guests explore how language models answer questions, how knowledge tools like Notion organize information, or how AI products find and compare content, the underlying step is the same: text is converted into embeddings — numerical vectors — so that similar ideas end up close together and can be found, compared, and reasoned over programmatically.