What is Embeddings?

Embeddings are numeric vector representations of text, images, or other data that capture semantic similarity. Embeddings are used to compare meaning between a query and documents.

Quick definition

Embeddings are numbers that represent the meaning of text so similar meanings end up close together.

How Embeddings works

• Embeddings are produced by a model that maps input into a fixed-length vector.
• Embeddings can be compared using similarity measures to find related items.
• Embeddings are commonly used for semantic search and vector search.
• Embeddings support retrieval-augmented generation (RAG) by improving retrieval quality.

Why Embeddings matters

Embeddings matters because embeddings enable retrieval based on meaning instead of exact keywords.

Embeddings can improve contextual relevance in AI search results and in RAG pipelines.

Example use cases

• Finding documents related to a question even when wording differs.
• Clustering prompts by similarity to analyze prompt coverage.
• Retrieving relevant passages for an LLM answer.

Related terms

• Vector Search
• Semantic Search
• Retrieval-Augmented Generation (RAG)
• Contextual Relevance
• Large Language Models (LLMs)