# Semantic search

> Semantic search: Finding documents by comparing embeddings of the query and the documents, so matches can share meaning without sharing words.

Canonical: https://llmdojo.dev/glossary/semantic-search

Finding documents by comparing embeddings of the query and the documents, so matches can share meaning without sharing words.

Learn it in the lesson: [Using embeddings and wrap-up](https://llmdojo.dev/learn/model-foundations/embeddings/using-embeddings) (Model Foundations · Embeddings: From Token IDs to Vectors).

## Interview questions about Semantic search

- [Your assistant's semantic search over user content is slow and expensive. How do you optimise it: embedding choice, ANN indexing and sharding?](https://llmdojo.dev/interview/q/your-assistant-s-semantic-search-over-user-content-is-slow-and-expensive)

## Related
- [Embedding](https://llmdojo.dev/glossary/embedding)
- [Embedding table](https://llmdojo.dev/glossary/embedding-table)
- [Cosine similarity](https://llmdojo.dev/glossary/cosine-similarity)
- [Dot product](https://llmdojo.dev/glossary/dot-product)
- [Contextual embedding](https://llmdojo.dev/glossary/contextual-embedding)
- [Glossary of every term](https://llmdojo.dev/glossary)