RAG (Retrieval-Augmented Generation)explained

Retrieval-Augmented Generation solves a core LLM problem: the model doesn't know your business. With RAG, when a user asks something, the system first retrieves the most relevant chunks from your own content — policies, product docs, past tickets — and hands them to the model as context, so the answer is based on your actual information.

This is what powers a support bot that answers from your help centre, or an internal assistant that knows your processes. Crucially, you can update the knowledge by just updating the documents — no retraining the model, no engineering cycle. It's cheaper, faster, and easier to keep current than fine-tuning.

For a founder, RAG is usually the right first step when you want AI that speaks to your specific data. It reduces hallucination because the model is quoting from real sources, and you can show users where an answer came from — which builds the trust an AI feature lives or dies on.