AI Chatbot Glossary
What Is 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.
Definition
A technique that enhances AI responses by first retrieving relevant information from a knowledge base, then using that information as context when generating a response. RAG reduces hallucinations and ensures chatbot answers are grounded in accurate, up-to-date data from your documents.
Why RAG (Retrieval-Augmented Generation) Matters for AI Chatbots
RAG is the single most important technique in modern chatbot engineering. Without it, the model invents plausible-sounding answers about your business. With it, the model quotes from your actual pricing, policies, and product catalog. Chatonbo uses RAG by default for every bot.
See it in practice:
See how RAG works at ChatonboRelated Terms
Knowledge Base
A knowledge base is a structured collection of information (documents, FAQs, policies) that a chatbot uses to find accurate answers — searched in real time to ground responses.
Embedding
An embedding is a numerical representation of text in a high-dimensional vector space — used to measure semantic similarity between pieces of text.
Vector Database
A vector database stores and searches high-dimensional vector embeddings efficiently — powering the semantic search in RAG-based chatbots.
Hallucination
A hallucination is when an AI model generates information that sounds plausible but is factually incorrect — a key risk in generative AI chatbots, mitigated by RAG and strict prompts.
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