Summary: RAG lets an AI answer questions using your own documents, with sources. Here is how it works and why it is the foundation of most business AI assistants.
Large language models know a lot about the world but nothing about your company's policies, products or processes. Retrieval-augmented generation (RAG) fixes that by giving the model the right information at the moment a question is asked.
How it works
- Your documents are split into small passages and indexed.
- When someone asks a question, the system finds the most relevant passages.
- The AI writes an answer using those passages and shows where each fact came from.
Why businesses like it
- Answers stay grounded in approved content.
- Updating a document updates the answers.
- Citations let staff verify information quickly.
- Permissions can control who sees what.
Get the content right
Remove outdated documents, give each file a clear owner and convert scanned PDFs into searchable text. Poor content leads to poor answers.
Test before launch
Collect real questions from staff with known correct answers and use them to measure accuracy.
FAQs
Is RAG the same as training a model?
No. RAG retrieves your content at question time; the model itself is not retrained.
Can it handle confidential documents?
Yes, with access controls and private deployment options.
What if the answer is not in our documents?
The assistant should say so and suggest who to ask.
