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
Ship Embeddings-optimized articles automatically
Mentionwell handles Embeddings on every published article — alongside the other six optimization targets in this glossary — so you don't have to think about it per post. Drop a domain, approve the first headline, watch the pipeline run.