Retrieval Augmented Generation (RAG): This is a popular technique where a large language model (your "common DNN") doesn't just generate answers from its internal training data. Instead, it first retrieves relevant information from external knowledge bases (like your structured classifications or vector databases of new news) and then uses that retrieved information to formulate its answer. This makes the LLM more accurate, up-to-date, and less prone to "hallucinations."