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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.

Sources coveringTowards 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 ScienceJune 13, 20261 min read

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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