Reduce RAG Costs with Query-Aware Compression on Bedrock

Running Retrieval Augmented Generation (RAG) at scale often incurs significant costs due to the large number of input tokens processed. This article introduces an optimization pattern on Amazon Bedrock designed to mitigate these expenses. The technique involves a "query-aware context compression" step: after initial retrieval, a smaller, more efficient language model is employed to filter the retrieved data chunks. This smaller model assesses the relevance of each chunk against the user's query, discarding less pertinent information. Only the most relevant, compressed context is then passed to the primary, larger language model for generating the final answer. This method effectively reduces the input token count, thereby lowering operational costs, all while preserving the accuracy and quality of the generated responses.