Cut RAG Costs with Query-Aware Compression
π‘Learn how to reduce RAG input-token costs without discarding the context your answers need.
β‘ 30-Second TL;DR
What Changed
The pattern compresses retrieved context after retrieval and before final generation.
Why It Matters
This pattern can improve the economics of production RAG systems, especially when retrieved documents are long or numerous. Teams may trade a small amount of preprocessing latency for lower primary-model inference costs.
What To Do Next
Prototype a query-aware filtering stage in your Amazon Bedrock RAG pipeline and measure token savings against answer-quality changes.
Key Points
- β’The pattern compresses retrieved context after retrieval and before final generation.
- β’A smaller model evaluates retrieved chunks against the user query.
- β’Reducing input tokens can lower RAG operating costs at scale.
- β’The approach aims to preserve answer quality while removing irrelevant context.
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Original source: AWS Machine Learning Blog β
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