What is RAG

Large Language Models (LLMs) like ChatGPT are incredible, but they have a major flaw: they are frozen in time. They only know what they were trained on, which means they don't know your private company data or today's news. This is where RAG (Retrieval-Augmented Generation) changes the game.

How RAG Works in 3 Steps

RAG creates a bridge between the AI and your live data. Here is the simple workflow:

• Retrieval: When you ask a question, the system searches your company’s database (PDFs, emails, docs) for the most relevant paragraphs.

• Augmentation: It combines your question with those retrieved paragraphs into a single prompt.

• Generation: The AI answers your question using only the facts it just found, ensuring the answer is accurate.

"If standard AI is taking a test from memory, RAG is taking an open-book exam."

Why It Matters

For businesses, RAG is the key to trusted AI. It allows you to build chatbots that can answer questions like "What is our vacation policy?" or "How did Q3 sales compare to Q2?" accurately, without needing to spend millions retraining a new model.