Embeddings are how AI converts text into numbers that preserve meaning. Words with similar meanings get converted to similar numbers.
"dog" and "puppy" get very similar vectors. "car" gets a very different vector. Real embeddings use 1,536 dimensions, capturing enormously subtle semantic relationships.
Why This Enables Better Search
Once text is in vector form, you can measure semantic distance. "Cancel subscription" and "stop my plan" have similar vectors. "Cancel subscription" and "pizza recipe" have very different vectors.
This enables semantic search: find content most similar in meaning to the query — regardless of whether any words match.
In Chatsloop
When you train Chatsloop, every piece of content becomes an embedding stored in a vector database. When a visitor asks a question, it becomes an embedding, the most similar content is retrieved, and that content generates the answer.
Better content = more accurate embeddings = more relevant retrieval = more accurate answers. Embeddings are why the chatbot can answer customer questions accurately even when the phrasing does not exactly match your content.
