Retrieval-Augmented Generation (RAG) in Plain English
RAG is one of the most useful patterns for business AI. It lets a language model answer questions using your documents rather than only its general training.
This article explains the idea without jargon and shows why it matters for accuracy.
The Core Idea
Before the model answers, the system searches your knowledge base for the most relevant passages and hands them to the model along with the question. The model then answers based on that supplied material.
Why It Helps
- Answers reflect your current, specific information.
- It greatly reduces invented facts.
- You can show sources, so answers are checkable.
- You update the documents, not the model, to change answers.
The Steps Behind the Scenes
- Your documents are split into small chunks and indexed.
- A user asks a question.
- The system retrieves the most relevant chunks.
- The model writes an answer grounded in those chunks.
Frequently Asked Questions
Does RAG remove all mistakes?
No, but it sharply reduces them by grounding answers in real source text you control.
How fresh can the answers be?
As fresh as your documents — update them and the next answer reflects the change.
If you need a hand with any of this, your Progressive Robot delivery team is ready to help. Raise a ticket from the Support area of your client portal or speak to your account manager and we will guide you through the next steps.