What are embeddings in AI?
Embeddings are lists of numbers (vectors) that represent the meaning of text, images or other data, so that similar items end up close together. AI systems use embeddings to compare meaning, power semantic search and retrieve context for language models.
Example
"Reset my password" and "I forgot my login" have similar embeddings even though they share few words.
Uses
| Use | How |
|---|---|
| Semantic search | Find related content |
| RAG | Retrieve context |
| Clustering | Group similar tickets |
| Deduplication | Spot duplicate issues |
| Recommendations | Similar items |
Considerations
Choose a model suited to your languages and domain, and re-embed when content changes.
