Reduce RAG costs on Amazon Bedrock with query-aware compression

发生了什么

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.

摘要按规则整理自下方来源原文

来源