Abstract
<jats:p>The article presents a method for the automated standardization of unstructured technical names for fasteners using Large Language Models (LLMs). The system architecture is based on the local inference of the Mistral-7B model via the LM Studio server, ensuring the confidentiality of industrial data. A comparative analysis is conducted between the «Instructor» method utilizing Pydantic validation and a proprietary direct JSON serialization method based on Few-Shot Prompting. Experimental results demonstrate that precise prompt engineering and in-context learning achieve 100% accuracy in generating names aligned with international DIN/ISO and DSTU standards. The proposed solution automates the updating of SQLite3 databases, minimizes human error, and provides correct multilingual localization of technical nomenclature. This approach significantly enhances data quality and operational efficiency within supply chain management systems.</jats:p>