RAG: how to make AI answer with your own documents

Language models know a lot about the world but nothing about your company. RAG is the bridge: it lets AI answer using your real documentation instead of making things up.

RAG stands for «retrieval-augmented generation». Instead of retraining a model —expensive and slow—, you retrieve the relevant fragments of your documents and give them to the model so it answers with grounding and citations.

How it works, plainly

Your documents are split into fragments, turned into vectors and stored in a database. When someone asks, the system finds the most relevant fragments and the model drafts the answer from them.

Data quality rules

A RAG is only as good as the documentation that feeds it. Before deploying, it's worth cleaning sources, removing obsolete versions and structuring key information.

Typical use cases

• Customer service and technical support
• Internal corporate knowledge search
• Assistants for sales or legal teams
• Employee onboarding

Privacy and deployment

For sensitive data, RAG can be deployed in private environments within the EU, keeping control and complying with GDPR. At AxisOne we design, evaluate and put corporate RAG into production with citations and traceability.