# What is Embeddings? Vector Embeddings & Semantic Search, explained

> Meaning as coordinates.

A vector embedding is a numerical representation of meaning — a sentence, paragraph, or document mapped to a point in high-dimensional space. Semantic search uses embeddings to find passages that mean the same thing as a query, even with no shared keywords. Every modern AI search product (Perplexity, ChatGPT Search, AI Overviews) leans on embeddings to retrieve.

## How Embeddings differs from RAG, GEO, LLMO

Keyword search matches strings. Semantic search matches meaning — embeddings let "how do I lower my heart rate" retrieve a paragraph titled "reducing resting pulse" even with zero shared words.

## How Mentionwell handles Embeddings

- Per-article embeddings indexed for semantic retrieval inside RAG-style pipelines.
- Embedding similarity drives internal linking — related articles surface each other automatically.
- Markdown mirrors so retrieved chunks are clean text rather than HTML.

## Frequently asked questions about Embeddings

### What is a vector embedding?

A numerical representation of meaning — text mapped to a point in high-dimensional space, where semantically similar text lives close together.

### Why do embeddings matter for AI SEO?

Every AI search product uses embeddings to retrieve passages relevant to a query. Pages that embed cleanly (clear topic, dense meaning, clean Markdown) retrieve more often.

## See also

- [RAG — Retrieval-Augmented Generation](https://mentionwell.com/rag): Grounding answers in retrieved sources.
- [GEO — Generative Engine Optimization](https://mentionwell.com/geo): Be the cited source.
- [LLMO — LLM Optimization](https://mentionwell.com/llmo): Be reachable, parseable, ingestible.


---

Canonical URL: https://mentionwell.com/embeddings
Live HTML version: https://mentionwell.com/embeddings
Site index for AI ingestion: https://mentionwell.com/llms.txt
Full reference: https://mentionwell.com/llms-full.txt