# What Are Embeddings in AI? Definition | Gr8r | Gr8r Studio

> Embeddings are lists of numbers that represent the meaning of text or images, so software can find similar items. How they work and where AI products use them.

Source: https://gr8rstudio.com/glossary/embeddings

Glossary · AI

# Embeddings

Embeddings are lists of numbers that represent the meaning of a piece of text, an image or other data. Items with similar meaning get similar numbers, which lets software search and group content by meaning rather than exact words.

[Prototype semantic search](https://gr8rstudio.com/services/product-engineering)[All terms](https://gr8rstudio.com/glossary)

In one line

Embeddings are lists of numbers that represent the meaning of a piece of text, an image or other data. Items with similar meaning get similar numbers, which lets software search and group content by meaning rather than exact words.

Related

- [Vector database](https://gr8rstudio.com/glossary/vector-database)
- [RAG](https://gr8rstudio.com/glossary/retrieval-augmented-generation)
- [LLM](https://gr8rstudio.com/glossary/large-language-model)

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On this page

1. [Why embeddings matter](#s-0)
2. [How embeddings work](#s-1)
3. [Common embedding mistakes](#s-2)
4. [How Gr8r approaches embeddings](#s-3)

## Why embeddings matter

Keyword search misses results that use different words for the same idea. Embeddings let a system find "cancel my plan" when the help article says "end your subscription".

They power semantic search, recommendations, clustering and most RAG systems.

## How embeddings work

An embedding model converts each item into a vector, a long list of numbers. To find related items, the system compares vectors and returns the closest ones.

Vectors are usually stored in a vector database built for fast similarity search.

## Common embedding mistakes

Mixing vectors from different embedding models. Embedding huge documents as one block, which blurs their meaning. Forgetting to re-embed content when it changes.

## How Gr8r approaches embeddings

We choose an embedding model based on your content and languages, test search quality with real queries, and often combine embeddings with keyword search for better results.

Example

A product catalogue embeds every item description so shoppers searching for "waterproof walking boots" also see items described as "hiking shoes with a membrane".

FAQ

## Questions

Ask anything else

Reading our answers…

- Are embeddings the same as a language model?
No. Embedding models turn content into vectors for comparison. Language models generate text. RAG systems usually use both.
- Do embeddings work across languages?
Many modern embedding models are multilingual, but quality varies by language, so it is worth testing with your own content.

## Related terms.

- [ 01 Vector databaseA vector database stores embeddings and finds the items most similar to a query quickly. It is the search layer behind many semantic search, recommendation and retrieval-augmented generation features. Explore ](https://gr8rstudio.com/glossary/vector-database)
- [ 02 RAGRetrieval-augmented generation (RAG) is a technique where an AI system first searches your own documents or data for relevant passages, then gives those passages to a language model so its answer is grounded in your information. Explore ](https://gr8rstudio.com/glossary/retrieval-augmented-generation)
- [ 03 LLMA large language model (LLM) is an AI model trained on very large amounts of text to predict and generate language. It can draft, summarise, classify, extract and reason over text, but it can also produce confident errors. Explore ](https://gr8rstudio.com/glossary/large-language-model)

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