The problem RAG solves
A general AI model does not know your pricing, your policies or last week's product update. Ask it anyway and it may fill the gap with something plausible but wrong. Retrieval-augmented generation (RAG) fixes this by finding the relevant parts of your own content first and giving them to the model as the source for its answer.
How it works
- Your documents are split into small passages.
- Each passage is turned into an embedding, a list of numbers that captures its meaning.
- When someone asks a question, the system finds the passages closest in meaning.
- The model writes an answer using only those passages and can point to where each fact came from.
What makes RAG good in practice
- Clean sources: outdated or contradictory documents produce contradictory answers
- Sensible chunking: passages should keep a complete idea together, like one policy or one FAQ
- Citations: showing the source lets users check an answer in one click
- Permission checks: people should only retrieve documents they are allowed to see
- An honest "I don't know": if nothing relevant is found, the assistant should say so
Measuring quality
Collect fifty real questions from your team or customers, write the correct answers, and test the system against them every time you change something. This small test set will tell you more than any demo.
Where to use it
Internal help desks, sales enablement, onboarding guides and customer support are natural fits. Anywhere people search through documents to answer questions, RAG can save time while keeping answers grounded in your own information.