Retrieval-Augmented Generation (RAG)

What is Retrieval-Augmented Generation (RAG)?

A method where an AI retrieves relevant snippets from a trusted source, then uses a language model to generate an answer grounded in that retrieved content.

Retrieval-Augmented Generation (RAG) is the technique behind most modern business chatbots. Instead of answering only from what a language model learned during training, a RAG system first searches a defined source — your help docs, product pages, FAQs — retrieves the most relevant passages, and feeds them to the model as context. The model then writes an answer using that material. The practical payoff is grounding: replies stay tied to your real content, so the bot is far less likely to invent facts. For a website chatbot, RAG is what lets it answer “what’s your return window?” from your actual policy page rather than guessing. The quality of a RAG bot depends almost entirely on the quality of the source it retrieves from — thin or outdated content produces thin or outdated answers, no matter how capable the model is.

BSG covers this in more depth in Top AI Tools for Sales & Customer Support: BSG\'s Best Picks.

Related terms

• Conversational AI
• AI Hallucination
• Knowledge Base

Related BSG products

• AI Support Assistant
• Conversational AI Voice