Retrieval-Augmented Generation
A technique where an AI retrieves relevant passages from a trusted knowledge base and uses them to generate accurate, grounded answers.
Glossary/Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) combines two steps: first the system retrieves the most relevant pieces of information from a trusted source — your help center, docs or past conversations — then it generates an answer using those passages as context. This grounds the AI's response in real, current facts instead of relying only on what a model learned during training.
For customer support, RAG is what makes AI answers trustworthy. Because the model is told to answer from retrieved content and cite it, it is far less likely to hallucinate a policy or invent a price. When no relevant content exists, a well-designed RAG system says it doesn't know rather than guessing.
BotIQ uses RAG over your own knowledge base, returning source-cited answers — so responses stay accurate and update automatically whenever you edit your content.
Frequently asked questions
Why does RAG reduce AI hallucinations?
RAG grounds the model's answer in passages retrieved from a trusted source and instructs it to answer from that content. Because the response is built from real, cited material — and the system can decline when nothing relevant is found — it's far less likely to fabricate.
Related terms
See Retrieval-Augmented Generation at work in LiveBotIQ
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