What is RAG (Retrieval-Augmented Generation)? And what can RAG do for you?
When AI is expected to provide answers based on extensive or frequently changing information, traditional models often fall short. RAG (Retrieval-Augmented Generation) solves this challenge. It allows AI to generate responses based on external sources, such as documents, databases, or internal knowledge bases, even if the AI wasn’t trained on that information.
How does RAG work?
RAG is made up of three steps:
- Retrieval:the AI first searches for relevant information from connected sources, such as policy documents, product manuals, or internal FAQs.
- Augmentation:the retrieved content is fed into the AI model as additional context. This step is what makes RAG special, the model doesn’t rely solely on what it “knows” from training, but gets enriched, real-time information to base its answer on.
- Generation:using this combination, its internal language capabilities plus the retrieved data, the AI produces a natural-language response.
In short: RAG = Retrieval + Augmented context + Generation.
This augmentation step allows the AI to remain accurate, context-aware, and aligned with your current knowledge, without having to retrain the model.
Why does this matter?
Without RAG, AI relies only on what it learned during training , which is often outdated or generic. With RAG:
- The AI stays up to date,
- It can work with organization-specific content,
- You get answers that are not only generally accurate but factually correct according to your own sources.
Cost-effective and scalable
One of the most compelling advantages of RAG is its cost efficiency. Traditional AI models require extensive retraining or fine-tuning to incorporate new knowledge, a process that is both time-consuming and expensive. With RAG, however, you can update the underlying knowledge simply by modifying the external sources the model retrieves from. This means you can scale and update your AI capabilities without incurring high operational costs. Instead of constantly adapting the model, you adapt the content, making RAG a highly sustainable and budget-friendly solution for dynamic or growing organizations.
Practical examples
- Employee asks about remote work policy
An employee asks: “How many days am I allowed to work from home?”
Thanks to RAG, the AI retrieves the latest HR policy and responds:
“According to the March 2024 remote work policy, you may work from home up to 3 days per week in agreement with your manager.” - Customer inquiries about accounting software
A customer asks: “Does your accounting package support multiple entities? And what does it cost?”
The AI retrieves information from the product brochure or pricing document and replies:
“Yes, our accounting software supports multiple entities under one account. The standard license is €39 per month, including support and updates.” - User asks how to reset their password
A user asks: “How do I reset my password?”
The AI searches internal IT support documentation or FAQs and replies:
“You can reset your password by going to the login page and clicking on ‘Forgot password’. You’ll receive an email with a reset link. If you don’t receive it within a few minutes, please contact IT support.”
RAG vs traditional AI answers
| Without RAG | With RAG |
|---|---|
| Based on training data only | Based on real-time internal content |
| Vague or generic answers | Specific and source-backed responses |
| Not transparent | Traceable to original documents |
Reliable AI with access to your knowledge
RAG is ideal for organizations that want to use AI as a smart assistant, with control over the content it uses. Whether it’s HR, IT, product documentation, or the details of your service: RAG enables your AI to access the right knowledge at the right moment, without needing to embed everything into the model itself.
