Recent discussions among experts highlight the importance of Retrieval-Augmented Generation (RAG) in enhancing large language models (LLMs). A new method introduced by Meta, known as Meta-Chunking, aims to improve text division by grouping related sentences, thereby preserving logical flow between sentences and paragraphs. Experts emphasize that RAG not only helps in referencing sources but also offers advantages in cost and speed, particularly when processing large amounts of tokens. The debate surrounding RAG versus long context is noted as misguided, as longer contexts are essential for optimizing RAG's effectiveness. Overall, the consensus is that RAG is crucial for the future of LLMs, as it addresses inherent limitations in prompt engineering and context management.