---
language: en
title: "Embeddings"
description: "Embeddings represent texts as vectors to compare relationships in meaning. Learn about their role in search and what to watch for when evaluating similar results."
canonical: "https://tropk.ai/en/resources/glossary/embeddings"
last_modified: "2026-10-12T16:13:11.231Z"
---

# Embeddings

Reviewed on 2026-10-06.

## What it means

Embeddings are numerical representations of objects, such as texts, produced by a model. In text applications, each sentence, passage, or document becomes a vector: a list of numbers that makes it possible to compare relationships between inputs. Queries and content represented in a compatible space can be ranked by similarity, making it possible to find related texts even when they are worded differently.

## Why it matters for marketing

For marketing, embeddings help explain why a search can bring different expressions closer together or group customer comments by topic. This makes it possible to evaluate results beyond repeated words. The team should check whether the match reflects the actual need, because similar texts can also describe opposite conditions, incompatible products, or information from a different time.

## Example in practice

Imagine a furniture store that receives the query chair for working all day. Its catalog describes an ergonomic office chair without repeating the question. Embedding-based search places that description among the candidates. The team checks the product attributes and decides to keep the result, also testing queries about decorative chairs to detect unsuitable matches.

## How to apply or verify

Ask the technical lead for the model, its version, the vector dimensions, and the comparison function. Use representative queries with expected results and examine the returned candidates, not just the similarity scores. Confirm that queries and documents use compatible representations. When changing the model, assess whether the stored embeddings need to be generated again before comparing results.

## Important distinctions

An embedding is neither a ready-made answer nor a fact check. Similarity measures proximity in the representation used, not truth, editorial quality, or likelihood of purchase. The [vector database](/en/resources/glossary/vector-database) stores and queries these representations; the model produces them. Keyword search and vector search can be combined, and observing a public result does not reveal which internal representation the system used.

## Sources and references

- [Vector embeddings](<https://platform.openai.com/docs/guides/embeddings>) — OpenAI

## Continue a leitura

- [Vector database](</en/resources/glossary/vector-database>)
- [Semantic search](</en/resources/glossary/semantic-search>)
- [Chunking](</en/resources/glossary/chunking>)

## About the author

[Vinicius Pacheco](</en/team/vinicius-pacheco>)

Responsible for Tropk’s technical content, focusing on crawling, indexing, structured data, and agent access to content.

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