What is Embedding in AI? – Definition and Meaning

## Definition
Embedding refers to representing data such as words, sentences, or images as vectors in continuous space for AI model interpretation.

## How It Works
Embeddings map similar meanings closer in vector space, allowing better semantic understanding in NLP and vision models.

## Examples or Use Cases
Used in recommendation systems, search ranking, and GPT embeddings API.

## Related Terms
– [Tokenization](#)
– [Vector Database](#)
– [Neural Network](#)

## Summary
Embedding transforms abstract data into machine-readable vectors, forming the foundation for semantic AI tasks.