VARG has intrinsic challenges that make it difficult to implement effectively, contrary to the notion that it is a universal solution. While use cases for RUG persist, particularly in contexts that require explainability, long context handling and caching are emerging as critical for optimizing performance. As interactions with models evolve, especially with increasing context sizes, caching offers the unique advantage of saving substantial resources. Utilizing tools that support long prompts can dramatically increase efficiency, allowing for extensive data representation without overwhelming costs. In practice, this means managing vast quantities of tokens, such as those equivalent to lengthy conversations or extensive written works, while minimizing operational expenses.

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