Definition

Embeddings are how AI changes things like words or images into number lists (vectors). These numbers show the meaning, style, or context of the input, which lets the AI compare or search for things that are similar—even if the exact words are different.

Example

The words ‘happy’ and ‘joyful’ have similar embeddings, so AI knows they mean almost the same thing.

How It’s Used in AI

Embeddings are used in search, chatbots, recommendations, and tools like semantic search. They let AI find the best match—not just exact matches—based on meaning. You’ve seen embeddings at work when ChatGPT remembers the context or when a site recommends articles that feel related.

Brief History

Word embeddings became popular in 2013 with Word2Vec by Google. Later models like GloVe, BERT, and OpenAI’s embedding APIs made embeddings more accurate and flexible for deeper AI understanding.

Key Tools or Models

Popular tools include Word2Vec, BERT embeddings, OpenAI Embedding API, and Hugging Face Transformers. Embeddings are also used in vector databases like Pinecone and Weaviate.

Pro Tip

Embeddings are the secret behind smart AI search. Use them when meaning matters more than matching words.