What is RAG and What It Is NOT?

Imagine your new employee asks: 'What's our parental leave policy?' Instead of searching through hundreds of HR documents, they type the question into your company's AI assistant. Within seconds, they get the exact answer with a link to the official policy document. That's RAG—Retrieval-Augmented Generation—in action.

Here's what RAG IS: Think of it like a smart librarian combined with a writer. When you ask a question, RAG first searches your company's library (all your documents, emails, databases) to find the relevant pages. Then, it hands those pages to an AI that reads them and writes a clear, personalized answer for you. Example: An employee asks 'Can I work remotely from Spain?' RAG finds your remote work policy, reads the international section, and answers: 'Yes, for up to 30 days per year according to our Remote Work Policy 2024, Section 4.2.'

RAG is NOT a crystal ball that 'knows' everything about your company. Unlike a human employee who remembers training from last month, RAG has zero memory between questions. Every time someone asks something, RAG starts fresh—it searches your documents again and builds a new answer. Example: If your HR updates the vacation policy today, RAG will find and use the new version tomorrow. A traditional AI model would still reference outdated information unless completely retrained.

Here's what makes RAG practical: It works with what you already have. Your existing policy PDFs, product manuals, support tickets, contracts, meeting notes—RAG can use all of it without you rewriting anything. Example: Your sales team has 500 product spec sheets scattered across SharePoint. Instead of forcing salespeople to remember everything, they ask RAG: 'Does our Enterprise plan include SSO?' RAG searches those 500 documents, finds the answer in the Enterprise Features sheet, and responds instantly.

RAG is NOT a substitute for good data management. Think of it this way: If your filing cabinet is a mess—documents mislabeled, outdated files mixed with current ones, important papers locked away where nobody can find them—RAG will struggle just like your employees do. Example: If your 2019 compliance policy and 2024 compliance policy are both named 'compliance.pdf' in different folders, RAG might retrieve the wrong one. Your AI is only as organized as your data.

RAG is NOT perfect—it can still make mistakes. If the documents RAG finds are unclear, contradictory, or incomplete, the AI might confidently give you a wrong answer. Example: Imagine your vacation policy says '15 days annual leave' in one document but an old email thread mentions '12 days.' If RAG retrieves both, it might get confused. This is why citation tracking matters—you need to see which documents RAG used, so you can verify the answer yourself.

RAG has real costs and speed considerations. Every question triggers two steps: searching your knowledge base and running an AI analysis. More complex questions with longer answers cost more. Example: A simple question like 'What's our office address?' is cheap and fast. But 'Summarize all customer complaints from Q3 and suggest improvements' requires RAG to search through hundreds of documents and process massive amounts of text—that takes time and computing power.

When implemented correctly, RAG transforms how your teams work. Your finance team gets instant answers about expense policies. HR can point employees to the right benefits instantly. Legal can search through thousands of contracts in seconds. Sales teams access product information without interrupting engineers. Example: Before RAG, finding the answer to 'What's our SLA for enterprise clients in EMEA?' might take 3 emails and 2 hours. With RAG, it takes 10 seconds. But this only works if your documents are organized, up-to-date, and accessible—RAG amplifies your knowledge management, whether it's excellent or chaotic.