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This article explores using Large Language Models (LLMs) to significantly reduce false positives generated by static code analysis tools. By prompting LLMs with code snippets and the analysis findings, researchers demonstrated LLMs could accurately assess whether a flagged issue is a genuine bug or a harmless occurrence, leading to a substantial decrease in developer alert fatigue. This approach offers a promising way to improve the efficiency and usefulness of static analysis in software development.