AI Chatbot Glossary
What Is Embedding?
An embedding is a numerical representation of text in a high-dimensional vector space — used to measure semantic similarity between pieces of text.
Definition
A numerical representation of text (words, sentences, or documents) in a high-dimensional vector space. Embeddings allow AI systems to measure semantic similarity between pieces of text, which is essential for knowledge base search, intent matching, and retrieval-augmented generation.
Why Embedding Matters for AI Chatbots
Embeddings are the reason RAG works. When a visitor asks "what is your return policy," the chatbot does not keyword-match — it converts the question into an embedding and finds the most semantically similar passage in your knowledge base. That is how "how do I send back my order?" finds the right answer even with different words.
Related Terms
RAG (Retrieval-Augmented Generation)
RAG is a technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer — reducing hallucinations and grounding replies in real data.
Vector Database
A vector database stores and searches high-dimensional vector embeddings efficiently — powering the semantic search in RAG-based chatbots.
Query Understanding
Query understanding is the AI step of interpreting what a user is asking — even when vague, misspelled, or phrased with slang — via semantic search plus language models.
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