Reduce RAG costs on Amazon Bedrock with query-aware compression

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Input tokens sent to the foundation model (FM) on every call are often a meaningful part of the cost of running Retrieval Augmented Generation (RAG) at scale. Query-aware compression offers one way to reduce how many of them reach the model. Amazon Bedrock provides the foundation models and features to build RAG applications. RAG retrieval usually tunes for high recall, returning a broad set of potentially relevant chunks so the primary model has thorough source material to work with. This design helps builders feel confident that the right information is available at inference time. As workloads scale, builders often look for ways to optimize the cost-performance tradeoff by reducing the number of input tokens the primary model processes while maintaining answer quality. The open, composable architecture of Amazon Bedrock supports custom post-retrieval processing steps that refine what reaches the primary model. In this post, we describe a post-retrieval customization pattern that achieves significant input-token reduction, and therefore cost savings, while preserving answer quality. It’s compatible with RAG retrievers on Amazon Bedrock, including Amazon Bedrock Knowledge Bases. As a secondary benefit, removing irrelevant context reduces the surface area for hallucination. After retrieval but before the final answer call, a smaller, lower-cost model on Amazon Bedrock filters retrieved chunks against the user’s query. The primary model then receives the filtered context and generates the answer. We cover the pattern’s architecture at a high level, show the core Amazon …

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