Meta's REFRAG Claims 30x Faster RAG by Filtering Context Before It Hits the LLM
A new technique from Meta called REFRAG reportedly compresses and filters retrieved context before passing it to a language model, achieving a 30.85x speed improvement while outperforming baseline LLaMA on accuracy.
Meta has published REFRAG, a retrieval-augmented generation technique that addresses one of the most persistent cost and latency bottlenecks in production RAG systems: the sheer volume of retrieved context that gets stuffed into the LLM's prompt. As @akshay_pachaar highlighted, REFRAG achieves a claimed 30.85x speed improvement by compressing and filtering relevant context before it reaches the language model, while reportedly outperforming LLaMA on downstream tasks.
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