RAG (Retrieval-Augmented Generation)

RAG is a technique that optimizes the output of an LLM by referencing an authoritative knowledge base outside its training data before generating a response.

Large Language Models (LLMs) are frozen in time; they only know what they were trained on up to their cutoff date. RAG solves this limitation by creating a hybrid system. When a user asks a question, the system doesn't rely solely on the AI's internal memory. Instead, it first searches a private 'Vector Database' containing your company's live documents (PDFs, Notion pages, SQL records). It retrieves the most relevant chunks of text and feeds them into the AI alongside the original question.

This process dramatically reduces 'hallucinations' (made-up answers) and allows the AI to cite its sources. For businesses, RAG is critical because it enables secure AI deployment without fine-tuning models. You can add or remove documents from the knowledge base instantly, and the AI's answers update immediately. It turns a generic model into a specialized expert on your proprietary data.