RAG与长上下文之间的斗争

Large Language Models face a fundamental limitation known as the knowledge cutoff, making them experts only on the world as it existed during their training phase. They remain unaware of private data or recent events. To address the issue of context injection, the industry is divided between two competing philosophies: one involves a complex engineering pipeline, while the other advocates for a brute-force architectural shift. Retrieval Augmented Generation (RAG) is an established solution but comes with significant engineering complexity, making its implementation challenging. In contrast, long-context approaches aim to enhance the model's ability to process longer inputs by altering the architecture directly. Each method has its pros and cons, and the choice depends on the specific needs of the application.