Language models do not know your company’s contracts, product manuals or internal procedures. RAG is the method of having the model read the right document before it answers.
What is the problem?
A language model only knows the data it was trained on. Ask it about your company’s return policy and it either says it does not know or, worse, invents a plausible-looking answer.
Retraining the model on your own documents is expensive and has to be repeated every time a document changes. RAG solves the problem in a simpler way.
How does RAG work?
- Preparation: Documents are split into small chunks and each chunk is turned into a vector that represents its meaning
- Retrieval: When a user asks a question, the chunks closest in meaning to the question are found
- Answer: Those chunks are given to the model together with the question; the model writes its answer based on them
What do you gain?
Answers rest on the current document, because when a document changes you only need to update the search index. You can show which document an answer came from, which makes verification easier. The model is less inclined to invent something that is not in the source.
What to watch for when setting it up
- If documents are messy or contradictory, the answers will be too; clean the content first
- If chunks are too small, context is lost; if too large, irrelevant text gets mixed in
- If not everyone should see every document, access permissions must be applied at the retrieval stage
For those who want to learn it
RAG is one of the most common building blocks of corporate AI projects today. In our Agentic AI with n8n training we build chunking, vector search and RAG-backed agents hands-on.