Retrieval-augmented generation (RAG) is a technique that improves AI accuracy by giving the language model access to a private knowledge base at query time. Instead of relying solely on training data, the model retrieves relevant documents from your internal data and uses them to generate a grounded answer — with citations. RAG is the standard approach for enterprise AI systems that need to answer questions about company-specific, proprietary, or frequently-updated information.
The problem RAG solves
Large language models like GPT-4o and Claude are trained on publicly available data up to a cutoff date. They know a lot about the world — but nothing about your company’s specific data:
- Your product documentation
- Your internal policies and procedures
- Your customer contracts
- Your CRM history