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This article addresses the limitations of Retrieval-Augmented Generation (RAG) by demonstrating that simply increasing context windows fails to resolve aggregation errors, proposing instead a hybrid architecture where deterministic full-scan engines handle computation queries while RAG manages retrieval. The proposed system routes specific tasks away from LLMs entirely to improve accuracy and error detection in large-scale data processing pipelines.

Surse care acoperăTowards Data Science, Interconnects, Slack Blog, AWS

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Larger Context Windows Don’t Fix RAG — So I Built a System That Does

Towards Data Science13 iunie 20261 min de cititArticolul și perspectiva sunt în engleză.

Increasing context size in RAG systems doesn’t improve accuracy for aggregation tasks—it makes errors harder to detect. In this article, I benchmark retrieval-based pipelines against a deterministic full-scan engine across 100,000 rows and show why computation queries must be routed away from RAG entirely. The post Larger Context Windows Don’t Fix RAG — So I Built a System That Does appeared first on Towards Data Science.

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