The Gen-Z framing makes this approachable, but one nuance that's worth adding is that RAG isn't really a "memory" system it's a retrieval system. The quality of the final answer depends far more on retrieval quality than most people realise. If the wrong chunks are retrieved, or relevant context never makes it into the prompt, even the best model can produce a confident but incorrect answer.

In production systems, teams often spend more time improving chunking strategies, metadata, hybrid search (keyword + vector), and reranking than swapping to a larger LLM. Better retrieval usually delivers a bigger accuracy gain than a bigger model because the model can only reason over the context it actually receives.