RAG (Retrieval-Augmented Generation)

RAG is a technique that lets an AI answer from your own documents instead of only its training data. Before the model replies, the system searches a knowledge base, your docs, help articles, past tickets, pulls the most relevant passages, and feeds them into the prompt. The model then answers grounded in that retrieved material.

For a founder this is the practical way to build an AI that knows your business. A support bot that cites your actual policies, an internal assistant that answers from your handbook, a sales tool that quotes your real pricing, all RAG. It matters because it cuts hallucination: the model is reasoning over facts you supplied, not guessing. It also means you update behaviour by updating documents, not retraining a model. Get the retrieval right, the quality of an answer is only as good as the passages you fetch, and RAG turns a generic model into one that speaks for your company.