This article explores the limitations of Large Language Models (LLMs) as sensors, highlighting the crucial distinction between perception and synthesis. It examines how traditional sensor architectures fail to capture the nuances of real-world data, leading to unreliable outputs and a failure to accurately represent the underlying structure of information. The article details a pattern of reduced RAG, where deterministic pipelines feed probabilistic components, rather than the other way around. It introduces the concept of “Sensors First, Synthesis Last” as a key architectural principle.
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