What is Retrieval-Augmented Generation (RAG)?
Retrieval-Augmented Generation, usually shortened to RAG, is a way of combining your own knowledge with AI language models so answers are based on real information, not guesswork. Instead of asking an AI to rely only on what it remembers from training, RAG allows the AI to look things up in a trusted knowledge base first, then generate a response using that information.In simple terms: RAG lets AI read your documents before it answers.
Why RAG Exists
Large language models are very good at writing text, but they have three important limitations:- They do not automatically know your documents
- They can confidently say things that are wrong
- They cannot easily show where information came from
How RAG Works (Step by Step)
At a high level, RAG follows a simple process:1
You create a knowledge base
Documents such as PDFs, reports, policies, or articles are uploaded and converted into a searchable format.
2
The system retrieves relevant information
When a question is asked, the system searches the knowledge base to find the most relevant passages.
3
The AI generates an answer
The AI uses the retrieved passages as context and writes a response based on them.
4
Sources can be shown
Because the answer is based on retrieved documents, it is possible to show where the information came from.
How RAG is Different from a Normal Chatbot
Standard Chatbot
- Relies mostly on its training data
- Cannot see your private documents
- May sound confident even when incorrect
RAG-Based System
- Actively retrieves relevant documents
- Grounds answers in real sources
- Can be updated by changing the knowledge base
- Reduces hallucinations and factual errors
What Makes RAG Powerful
Uses Up-to-Date Information
You can update or replace documents without retraining the AI model.
Improves Accuracy
Answers are constrained by retrieved evidence, which significantly reduces fabricated responses.
Supports Transparency
Retrieved sources can be inspected, cited, or audited.
Separates Knowledge from AI
The same knowledge base can be reused across websites, internal tools, chat interfaces, research workflows, and automated systems.
What RAG is Not
Why Ardie Uses RAG
Ardie is built around the idea that knowledge should be reusable, governable, and independent of any single interface. Using RAG allows Ardie to:- Turn documents into durable knowledge bases
- Serve that knowledge through APIs
- Let users connect their own AI tools
- Enable sharing or selling access without exposing infrastructure
When RAG is the Right Choice
RAG is especially useful when:| Scenario | Why RAG Helps |
|---|---|
| Accuracy matters | Answers are grounded in real documents |
| Information must be traceable | Sources can be cited and audited |
| Knowledge changes over time | Update documents without retraining |
| Same information used in many places | One knowledge base, many applications |
| You want control over AI responses | Constrain what the AI can and cannot say |