# Embeddings

Source: https://customlabs.io/glossary/embeddings/
Updated: 2026-09-13

Foundations

# Embeddings

Also known as vector embeddings

Embeddings are numeric vectors that place similar content close together in vector space.

They are the substrate under vector search and RAG, converting a query and a corpus into comparable vectors.

Embedding model choice and dimensionality directly bound how good retrieval can be.

A mismatched or stale embedding model is a common, silent cause of RAG failure.

[Insight: Your RAG demo lied to you](https://customlabs.io/insights/your-rag-demo-lied/)[Capabilities](https://customlabs.io/capabilities/)

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## Related terms

[Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) retrieves relevant passages at query time and feeds them into an LLM's context.](https://customlabs.io/glossary/retrieval-augmented-generation/)[Vector Search (Semantic Search) Vector search finds the nearest matches to a query by comparing embeddings, not exact keywords.](https://customlabs.io/glossary/vector-search/)[Chunking Chunking splits source documents into smaller passages before embedding, so retrieval returns focused text.](https://customlabs.io/glossary/chunking/)

## More in Foundations

[Context Window The context window is the maximum text, measured in tokens, a model can consider at once.](https://customlabs.io/glossary/context-window/)[Token A token is the basic unit of text an LLM reads and writes.](https://customlabs.io/glossary/token/)[Model-Agnostic Architecture A model-agnostic architecture lets an application swap or route between LLM providers without a rewrite.](https://customlabs.io/glossary/model-agnostic-architecture/)[Fine-Tuning (vs RAG) Fine-tuning trains a model's weights on your own examples, changing its behavior directly.](https://customlabs.io/glossary/fine-tuning/)