What Is RAG? How Businesses Keep AI Factual
If you want an AI assistant that answers from your real information — your prices, policies, catalogue — and never makes things up, the technology you need is called RAG. Here is how it works and why it matters.
The problem RAG solves
A raw language model only knows what it learned during training. It doesn't know your current prices or today's stock, and if asked, it may guess. For a business, a guessed price or policy is worse than no answer.
How RAG works, in three steps
- Store your knowledge. Your documents are converted into embeddings and kept in a vector database.
- Retrieve the relevant bit. When a customer asks a question, the system finds the most relevant passages from your documents — by meaning, not just keywords.
- Generate a grounded answer. Those passages are handed to the model along with the question, so it answers from your real facts rather than its memory.
Why it matters for business AI
RAG is what separates a toy chatbot from one you can put in front of customers. It means the assistant answers from your approved, current information; when the answer isn't in your documents, a well-built bot says so and offers a human — it doesn't invent.
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