Embeddings
Also known as: Vector Embeddings, Semantic Embeddings, Text Embeddings
In one sentence
Collections of numerical representations that capture meaning. When you have multiple embeddings, you can compare them to find similar content, power search systems, and enable AI to understand relationships between concepts.
Explain like I'm 12
Imagine turning every book in a library into a GPS coordinate. Books about similar topics would be close together on the map. Embeddings let computers 'see' which ideas are related by checking how close their coordinates are.
In context
When you search in ChatGPT, it converts your question into embeddings and compares them against embeddings of its knowledge to find relevant information. RAG systems store document embeddings in vector databases, then retrieve the closest matches when you ask questions. Recommendation engines use embeddings to find 'customers who liked X also liked Y'.
See also
Related Guides
Learn more about Embeddings in these guides:
Embeddings & RAG Explained (Plain English)
IntermediateHow AI tools search and retrieve information from documents. Understand embeddings and Retrieval-Augmented Generation without the math.
11 min readEmbeddings: Turning Words into Math
IntermediateEmbeddings convert text into numbers that capture meaning. Essential for search, recommendations, and RAG systems.
7 min readVector Database Examples: Real-World Use Cases and Code
IntermediatePractical examples of vector databases in action: semantic search, chatbot memory, recommendation systems, and more with code snippets.
9 min read