PromptSharp › Glossary › Embeddings
What is Embeddings?
Embeddings turn text into numeric vectors so similar meanings sit close together, powering semantic search and RAG.
An embedding is a list of numbers (a vector) that represents the meaning of a piece of text. Texts with similar meaning produce vectors that are close together, so software can measure semantic similarity mathematically rather than matching exact words.
Embeddings are the engine behind semantic search, recommendations, clustering, and the retrieval step in RAG: to find the passages most relevant to a question, a system embeds the question and finds the stored passages whose embeddings are nearest.
Frequently asked
What are embeddings used for?
Semantic search (finding text by meaning, not keywords), recommendations, deduplication, clustering, and the retrieval step in RAG. They let software compare meaning numerically.
Do I need to understand embeddings to use AI?
Not for everyday prompting. They matter when you're building search or a RAG system over your own documents. For writing prompts, the concepts of context and grounding are more relevant.
Related terms
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