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Developers building RAG systems on Bedrock can meaningfully reduce inference costs and latency without redesigning their retrieval pipeline, while business stakeholders can expect improved unit economics for LLM-powered applications at scale.
AWS Bedrock now supports a query-aware context compression pattern for RAG systems that uses a smaller model to filter retrieved chunks before the primary model processes them. This reduces input token consumption and operational costs while maintaining answer quality, directly addressing a major cost driver in production RAG deployments.
Read the full article at AWS AI/ML
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