Reduce RAG costs on Amazon Bedrock with query-aware compression
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What happened
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.
Summary assembled by rule from the sources below
Why it's spreading
Sources
Official
- Reduce RAG costs on Amazon Bedrock with query-aware compressionAWS Machine Learning