AWS Machine Learning Blog described a scheme for Amazon Bedrock that compresses context based on the user query. After fragment retrieval, the smaller model selects relevant parts from them, and then the main model generates the answer.
According to AWS, this order reduces the number of input tokens and RAG costs while maintaining the quality of the answer. The material discusses applying the approach in large-scale RAG systems.
Available data are presented as metadata and a brief description of the publication, not as the full text of the article. Therefore, the specific models, the size of the savings, the quality measurements, or the experimental conditions cannot be established from them.
editorial commentary
Why it matters
A likely consequence is that developers of RAG systems will gain an additional means to control costs through pre-filtering of context. The next observable signal will be AWS-released results on token savings and answer quality. A substantial uncertainty is related to the absence of specific metrics and the full text of the material in the available data.