RAG Explained: How AI Agents Learn from Your Own Business Data

A generic AI model knows a lot about the world, but nothing about your product, your customers, or your internal processes. RAG — Retrieval-Augmented Generation — is the bridge between generic AI intelligence and domain-specific business knowledge.

What is RAG?

RAG is a technique where an AI model, when generating an answer, doesn't just rely on its training data but actively retrieves relevant information from an external knowledge base — PDFs, a CRM, an intranet, or a database.

How Does it Work in Practice?

Why RAG for B2B AI Agents?

Conclusion

RAG is the technology that turns a generic AI into a domain expert — without risking your data and without the enormous costs of fine-tuning. For B2B AI agents working with internal knowledge, products, or customer data, RAG is almost always the recommended architecture.