LLMs are impressive but they have a fundamental flaw: they don’t know what they don’t know. Ask a model about something outside its training data, and it won’t politely say “I’m not sure.” Instead, it will fabricate a confident, plausible-sounding answer. This tendency has been one of the biggest challenges in deploying LLMs in production. Retrieval-Argumented Generation is one of the most practical solutions to this problem. Rather than hoping the model memorized the right information during training, RAG retrieves relevant context at query time and feeds it into the model alongside the user’s question. The model then generates a response grounded in that retrieved context, rather than relying solely on its parametric memory.