What is RAG? A private AI knowledge base, explained
RAG, short for retrieval-augmented generation, is a way to make an AI assistant answer from your own documents instead of its general training. It first finds the passages that match a question, then writes an answer from them and cites where each fact came from.
How RAG works, step by step
- Your documents are split into short passages and indexed by meaning.
- A question is matched against that index to find the most relevant passages.
- A language model writes the answer using only those passages, and links each claim to its source.
When a business should use it
RAG helps wherever people waste time searching: policies and SOPs, product catalogues, contracts, support history and internal reports. If your answers live in documents, RAG can put them one question away. Our private AI knowledge base service builds exactly this.
Keeping the data private
The documents, the search index and even the language model can all run on your own servers, so sensitive information never leaves your network. See local LLM deployment for how that part works.
RAG questions
Does RAG stop the AI from making things up?
It greatly reduces it, because answers are built from retrieved passages and cite them. A well-built system also says when it cannot find an answer instead of guessing.
Can RAG answer questions in Urdu?
It can, when the embedding and language models chosen for the project handle Urdu well. We test this on your own documents before recommending a setup.