Definition
RAG (Retrieval-Augmented Generation)
RAG is a technique that lets an AI model pull information from a specific, verified knowledge base before generating an answer, reducing incorrect or made-up responses.
Without RAG, an LLM answers purely from what it learned during training, which can be outdated or simply wrong for business-specific questions. RAG solves this by retrieving relevant, current information from your actual documents or database at the moment a question is asked, then having the model base its answer on that retrieved content — the same underlying approach ZAVYANS uses to build chatbots that answer from your verified knowledge base instead of guessing.
RAG in practice
A property developer's chatbot uses RAG to answer "What's the payment plan for the two-bedroom apartments?" Instead of guessing, it retrieves the current price list and payment plan document, writes its answer from that document, and can point to where the information came from.
Related terms
Common questions about RAG
Why use RAG instead of a plain AI model?
A plain model only knows what it learned in training and may guess. RAG looks up relevant information from your own documents first, so answers are current, specific and easier to verify.
What can be used as a RAG knowledge base?
Website pages, FAQs, product and price lists, policies, manuals and support tickets, split into searchable chunks.
Does RAG stop AI hallucinations completely?
No, but it reduces them considerably, especially when the system is set to say it doesn't know when the answer isn't in the source material.
How do you keep a RAG knowledge base up to date?
Update the source documents whenever prices, policies or services change, and set a regular review schedule. The chatbot's answers are only as current as the information it retrieves.
