What is Embedding?
Last updated May 2026A numerical representation of text that captures its meaning as a vector.
Definition
An embedding is a way to represent text (words, sentences, or documents) as a list of numbers (vector) that captures semantic meaning. Similar concepts have similar embeddings, making it possible to measure how related two pieces of text are. Embeddings are fundamental to search, recommendation systems, and RAG applications.
💡 Example
The embeddings for "dog" and "puppy" would be very close together in vector space, while "dog" and "refrigerator" would be far apart. This allows AI systems to understand that a search for "puppy care" is related to "dog health tips."
Related concepts
A technique that lets AI access external knowledge bases to provide more accurate answers.
Why this matters
Embeddings turn text into numbers that capture meaning — they're how AI tools understand that 'car' and 'automobile' are related. This concept powers semantic search, recommendation systems, RAG, and similarity matching across AI products.
Real-world example
When Perplexity searches for answers, it uses embeddings to find semantically relevant content, not just keyword matches. When NotebookLM understands your uploaded documents, it converts them to embeddings first. Better embeddings = better retrieval = better answers.
See it in action
A database optimized for storing and searching AI embeddings at scale.
What is Embedding?
An embedding is a way to represent text (words, sentences, or documents) as a list of numbers (vector) that captures semantic meaning. Similar concepts have similar embeddings, making it possible to measure how related two pieces of text are. Embeddings are fundamental to search, recommendation systems, and RAG applications.
How does Embedding work in practice?
The embeddings for "dog" and "puppy" would be very close together in vector space, while "dog" and "refrigerator" would be far apart. This allows AI systems to understand that a search for "puppy care" is related to "dog health tips."
How are embeddings used in AI applications?
Embeddings power semantic search (finding content by meaning rather than keywords), recommendation systems, document clustering, and duplicate detection. They are also the foundation of RAG systems, where relevant documents are retrieved by comparing embedding similarity.
What is the difference between word embeddings and sentence embeddings?
Word embeddings represent individual words as vectors, capturing relationships like synonyms. Sentence embeddings represent entire passages as single vectors, capturing the overall meaning. For most practical AI applications like search and RAG, sentence embeddings are more useful.
Do you need to understand embeddings to use AI tools?
Most users do not need to understand embeddings directly, as AI tools handle them behind the scenes. However, understanding embeddings helps when building custom search systems, setting up RAG pipelines, or evaluating why an AI retrieval system returns certain results.