A general-purpose AI model knows a lot about the world and nothing about your business. It has not read your policies, your pricing or your product manuals. RAG — retrieval-augmented generation — closes that gap by giving the AI access to your own information at the moment it answers.
Simple input, grounded response
The idea is easy to describe. Business information goes in; a response grounded in that information comes out. In between, a small number of steps make it work.
- Business information: PDFs, policies, manuals, FAQs and pricing.
- Document processing: the source material is structured for use.
- Knowledge base: organized business context is stored.
- Vector search and retrieval: the relevant context is found for each question.
- AI response: the answer is written from your business's own information.
Configure once, reuse everywhere
Because the knowledge layer is separate from any single channel, the same RAG infrastructure can support several services. A customer support helpdesk, an internal knowledge base and a website chatbot can all draw on the same organized knowledge. When the information changes, it changes in one place.
Where people fit in
Grounded answers do not remove the need for people. Good systems are designed to escalate — to hand a conversation to a person when a question falls outside the knowledge available or needs judgement. That handoff is part of the design, not an afterthought.
- RAG
- Knowledge base
- Customer support