AI6 min read
A practical introduction to RAG for business teams
Retrieval-augmented generation explained without the jargon: how it works, where it helps a business, and what it takes to make it dependable.
Large language models are good at language but know nothing about your business. Retrieval-augmented generation, usually shortened to RAG, closes that gap by finding the relevant documents first and asking the model to answer using only that material.
How it works
- Your documents are split into passages and indexed for meaning, not just keywords.
- A question is matched against that index to find the most relevant passages.
- The model writes an answer grounded in those passages, ideally with citations.
What makes it dependable
The quality of a RAG system depends far more on the data and the retrieval step than on the model. Clean source documents, sensible chunking, access controls that respect who can see what, and a set of test questions with known answers are what separate a demo from a tool people trust.
Start with a narrow, well-documented domain such as internal policies or product documentation, measure accuracy against real questions, and expand only once the answers are consistently right.