RAG solves one of the biggest limitations of AI models: they only know what was in their training data, which has a cutoff date and doesn't include your private information. RAG works by retrieving relevant documents from your own data sources and including them in the prompt alongside the user's question, so the model can base its answer on your actual, current information. When a customer asks about your returns policy, a RAG system finds the relevant policy document and provides it to the model, which then generates an accurate response. This approach has several advantages over fine-tuning: it uses up-to-date information, you can see exactly which documents informed the answer (improving trust and auditability), and you don't need to retrain the model when your information changes. However, RAG is only as good as its retrieval step. If the system fetches the wrong documents - or misses the right ones - the model's answer will be wrong or incomplete, potentially with great confidence. Building an effective RAG system requires careful attention to how documents are chunked, indexed and searched. It's deceptively simple in concept but genuinely challenging to get right at production quality.