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

# The Problem With AI's "Memory Upgrade" Companies trying to make AI smarter by giving it access to larger documents (a technique called RAG) discovered it actually makes mistakes *worse*, not better—and hides those errors so you can't even tell something went wrong. A better solution is to stop using AI for certain tasks altogether, like when you need to add up numbers or find specific information, and use traditional database methods instead. Think of it like using a calculator for math rather than asking someone to do complex arithmetic from memory.
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 en
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