What is RAG?
A Large Language Model knows a lot, but it doesn’t know your organization. Retrieval-Augmented Generation (RAG) gives AI access to up-to-date documents, knowledge bases, and company information. This way, answers are based on your own information rather than just general knowledge. The result is AI that’s more reliable, more relevant, and immediately applicable in day-to-day practice.
AI that works with your own knowledge
A standard AI model provides answers based on the information it was trained on. With RAG, AI first searches for relevant information within your organization, such as manuals, product information, work instructions, or technical documentation. Only then is an answer generated.
As a result, answers remain up-to-date and are better aligned with your organization. Moreover, you don’t need to retrain an AI model when documents change. Knowledge remains managed in one place, while AI always uses the most relevant information.
What does this mean for your organization?
RAG makes AI suitable for business applications where reliability is crucial. Employees find the right information faster, customers receive better answers, and knowledge remains centrally available, even when it’s spread across multiple systems.
RAG is particularly valuable when you want to deploy AI based on your own documents and processes. This allows you to derive more value from existing knowledge while maintaining control over the information on which AI bases its answers.
Curious about how you can integrate AI with the knowledge within your organization? Tell us what challenges you’re facing. Together, we’ll explore how your data can deliver more value.
