12 Sept 2026 · 1 min read
RAG explained: how to build a copilot that knows your business
Retrieval-Augmented Generation lets AI answer from your own documents and data — accurately and with sources. Here's how it works.
General AI assistants know a lot about the world, but nothing about your business — your products, prices, policies or customers. RAG (Retrieval-Augmented Generation) fixes that.
How RAG works, in three steps
- Retrieve. When someone asks a question, the system searches your documents and data for the most relevant information.
- Augment. That information is handed to the AI model along with the question.
- Generate. The model writes an answer based on your information — and shows the sources it used.
Why it matters
- Accurate: answers come from your own, up-to-date information.
- Trustworthy: every answer links back to its source.
- Secure: the copilot only sees what each user is allowed to see.
- Cost-effective: no need to train a custom AI model.
What you can build with it
- A knowledge copilot that answers questions from SOPs, policies and manuals.
- A data copilot that answers "which dealers haven't ordered in 30 days?" from live data.
- A sales copilot that briefs your team before every customer call.
The hard part isn't the AI — it's preparing clean, well-organised content and data for it to retrieve. That's where most of the work, and most of the value, lies.