Embeddings
Numerical representations of text that capture semantic meaning, allowing AI to find similar content and understand relationships.
What Embeddings Do
Humans understand that "dog" and "puppy" are related. Computers just see different letters. Embeddings bridge this gap by converting text into numbers that preserve meaning.
Similar concepts get similar numbers. "King" and "queen" end up close together. "King" and "banana" end up far apart.
How Embeddings Work
An embedding model converts text into a long list of numbers (often 1,000+ dimensions). Each number captures some aspect of meaning. The full list represents the text's semantic content.
Example: "The quick brown fox" might become [0.234, -0.891, 0.445, ...] while "A fast orange fox" gets similar numbers because the meaning is similar.
Why Embeddings Matter
Semantic search: Find documents by meaning, not just keywords. Search "revenue issues" and find documents about "declining sales."
RAG systems: Embeddings power the retrieval step. They find which documents are relevant to your question.
Clustering: Group similar items automatically. Customer feedback, support tickets, documents.
Recommendations: Find items similar to ones you liked.
Embedding Quality
Different embedding models have different strengths:
- Some are better for short text
- Some handle code well
- Some work across languages
- Size and speed vary significantly
When evaluating AI tools that use embeddings, search quality often depends on which embedding model they chose.