Embeddings are numerical representations of text that capture semantic meaning in a format computers can process. They enable AI systems to understand similarity, search for relevant information, and compare concepts mathematically.
How are embeddings used in voice AI?
When a caller asks a question, the query can be converted to an embedding and compared against a knowledge base to find relevant information. Embeddings power semantic search that understands meaning rather than just matching keywords. They also enable clustering similar conversations for analysis.
Why do embeddings matter?
Embeddings bridge the gap between human language and machine computation. They allow AI systems to recognize that “cancel my appointment” and “I need to reschedule” are related concepts even though they share few words. This semantic understanding is fundamental to flexible natural language processing.
Embeddings in practice
A customer asks: “What’s your return policy for electronics?” The question is converted to an embedding and compared against the company’s knowledge base. Even though no document is titled exactly that, the system finds the “Electronics Refund Guidelines” document because its embedding is semantically similar to the query.