What is RAG (Retrieval-Augmented Generation)?

Large language models know a lot in general but nothing about your specific business: your prices, policies, service area or customer history. RAG solves this by retrieving the most relevant pieces of your information at the moment a question is asked and giving them to the model along with the question.

In practice, your documents are split into small chunks and indexed so they can be searched by meaning. When a customer or employee asks something, the system finds the best-matching chunks and the AI writes an answer using them, often with a reference to where the information came from.

For small businesses, RAG powers website chat assistants that answer from your actual FAQs and policies, and internal assistants that answer staff questions from SOPs. Keeping the source information accurate and up to date matters as much as the AI itself.