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.
← Back to the full glossaryRelated terms
Retrieval-Augmented Generation (RAG) Retrieval-Augmented Generation (RAG) retrieves relevant passages at query time and feeds them into an LLM's context. Vector Search (Semantic Search) Vector search finds the nearest matches to a query by comparing embeddings, not exact keywords. Chunking Chunking splits source documents into smaller passages before embedding, so retrieval returns focused text.
More in Foundations
Context Window The context window is the maximum text, measured in tokens, a model can consider at once. Token A token is the basic unit of text an LLM reads and writes. Model-Agnostic Architecture A model-agnostic architecture lets an application swap or route between LLM providers without a rewrite. Fine-Tuning (vs RAG) Fine-tuning trains a model's weights on your own examples, changing its behavior directly.