As artificial intelligence systems become core components of everything from enterprise workflows to everyday tools, one thing is becoming crystal clear: context matters. It’s no longer enough for a model to simply string together grammatically correct sentences. To truly add value—whether as a legal assistant, an AI tutor, or a customer support bot—an AI system needs to deliver the right answer at the right time, grounded in real-world knowledge and tuned to the situation at hand.
That’s where two key techniques come into play: Retrieval-Augmented Generation (RAG) and Context-Aware Generation (CAG). These two approaches offer different solutions to the same challenge: how to make large language models (LLMs) smarter, more reliable, and more useful.
