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Could Retrieval-Augmented Generation with Large Language Models Help Make Local Zoning Codes Easier to Navigate?

Researchers at Urban tested retrieval‑augmented generation (RAG) with large language models on Minneapolis' 467‑page zoning code to see if AI can simplify permit queries. The benchmark showed that RAG‑enhanced models returned more accurate, context‑aware answers than baseline LLMs. City officials see potential for AI‑driven screener tools and chatbots to speed up permitting, yet they remain wary of unvetted outputs. The study extends earlier work on machine‑learning‑based zoning data compilation.