Glossary
RAG (Retrieval-Augmented Generation)
RAG is a technique that lets AI pull information from your own business data to generate accurate, context-aware answers instead of relying on general knowledge alone.
Retrieval-Augmented Generation (RAG) is a technique that improves AI accuracy by letting it look up information from your own data before generating a response. Instead of relying only on what the AI model learned during training, RAG retrieves relevant documents, product details, or policy information from your business databases and uses that as context.
How RAG works
- A question comes in
- The system searches your business data for relevant information
- It feeds that information to the AI model as context
- The AI generates an answer based on your actual data, not just general knowledge
Why RAG matters for small businesses
RAG is what makes custom AI assistants accurate for your specific business. Without RAG, an AI assistant would guess answers about your products, pricing, and policies based on general internet knowledge. With RAG, it looks up the actual information from your data and gives accurate, verifiable answers.