LLMs as scalable bounded intelligence: extracting signals from unstructured data
sh_reya · x · 2026-09-26
BEBischof offers a framing for the practical value of LLMs: for many datasets the goal is to extract a specific kind of signal, and much of that data can't be processed by a simple code snippet or even a complicated program.
In this application, language models are best understood as 'scalable bounded intelligence' — not human, but smart enough to carry out tasks that would be hard to cover with hand-written code. A concise mental model for positioning LLMs in data engineering and information extraction workflows.
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