RAG
RAG is a technique that gives language models access to new information from external sources to improve their answers. The system retrieves relevant documents and grounds the answer in current or internal knowledge, sometimes through a vector database.
On this page
Source content checked . English translation published 2026-10-27.
In business automation
RAG combines information retrieval with answer generation. It is useful when a bot needs to answer from policies, a catalog, or company documents that may change, without retraining the model for every update.
Examples
These examples illustrate a possible workflow. Adapt the connections and rules to your own systems.
Business process example
An employee asks about reimbursement for travel expenses. The system searches the relevant policy, passes the retrieved passages to the model, and returns an answer with a reference to the document. If the policy does not cover the case, the question is sent for review.
Relevant tools
- LlamaIndex
- Build document ingestion, indexing, and knowledge retrieval workflows for language applications.
What to check
- An outdated document or overly broad search permissions can produce a wrong answer or expose information. Search does not remove the need for access control.