Embeddings
Mathematical representations that convert text into numerical vectors, capturing semantic meaning so that similar concepts are positioned close together. The foundation behind semantic search and retrieval-augmented generation.
What Embeddings Actually Do
Computers don't naturally understand language—they work with numbers. Embeddings bridge this gap by converting words, sentences, or entire documents into arrays of numbers (vectors) that capture their meaning. The key insight: texts with similar meanings produce vectors that are mathematically close together, even when the actual words are completely different.
For example, "How do I get a refund?" and "I want my money back" use different words but produce embeddings that are nearly identical. This is what enables AI systems to understand that customers asking the same question in different ways should get the same answer.
Embeddings in Customer Service
Embeddings power two critical capabilities in AI customer service. First, semantic search—when a customer asks a question, the system converts it to an embedding and finds knowledge base content with similar embeddings, even if the wording doesn't match. Second, retrieval-augmented generation—the retrieved content (found via embeddings) is fed to the AI model to generate accurate, grounded responses.
The quality of embeddings directly affects how well an AI agent retrieves relevant information. Specialized embedding models trained on customer service data—like the Fin-CX-Retrieval model—produce better representations for support queries than general-purpose embedding models, leading to more accurate retrieval and higher resolution rates.