Imagine asking a company chatbot: “What is our refund policy?” The chatbot responds fluently and confidently. The problem is that it uses general information that may not match your company’s actual rules.
How can you make sure AI answers questions based on your documents, internal policies, or product information rather than relying only on what it learned during training?
One solution is RAG (Retrieval-Augmented Generation). It is an approach that combines information retrieval with AI-generated answers. This allows a language model to work with a company’s own data without having to train a new model every time the information changes.