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Amazon Bedrock新技巧降低RAG成本，提升效率。
Amazon Bedrock实现查询感知上下文压缩，通过在主模型回答前过滤检索到的片段，减少输入令牌和成本，同时保持答案质量。
原文 · AWS Machine Learning Blog
Reduce RAG costs on Amazon Bedrock with query-aware compression
Input tokens are often a meaningful part of the cost of running Retrieval Augmented Generation (RAG) at scale. This post describes a query-aware context compression pattern on Amazon Bedrock: after retrieval, a smaller model filters retrieved chunks against the query before the primary model answers, reducing input tokens and cost while preserving answer quality.