A tokenization method optimized for retrieval-augmented generation to balance efficiency and accuracy.
More about RAG Tokenization
RAG Tokenization refers to the process of splitting input text into tokens specifically optimized for frameworks like retrieval-augmented generation (RAG). Proper tokenization ensures that retrieval and generation components interact efficiently, minimizing token limits while retaining contextual relevance.
This method is essential for balancing the context window size and accuracy in tasks like knowledge-grounded generation and context-aware generation.