Reduce RAG costs on Amazon Bedrock with query-aware compression
AWS proposes using a cheaper filter model to compress RAG context before the primary model call
AWS describes a post-retrieval compression pattern for RAG on Amazon Bedrock where a smaller, lower-cost model filters retrieved chunks against the user query before the primary model generates an answer. The technique reduces input token costs at scale while maintaining answer quality and shrinking hallucination surface area. This is a practitioner-level optimization tip rather than a new product or research breakthrough.