Web application for classifying and assisting in incident management using OpenAI LLMs

• Autores: Vasquez Jaramillo, Diego Sebastian, Zavaleta Mego, Luis Daniel, Rosas Pérez, Luiggi Anthony, Mendoza De Los Santos, Alberto Carlos
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Universidad La Salle
• Repositorio: Revistas - Universidad La Salle
• Lenguaje: español
• OAI Identifier: oai:ojs.revistas.ulasalle.edu.pe:article/298
• Enlace del recurso: https://revistas.ulasalle.edu.pe/innosoft/article/view/298; https://doi.org/10.48168/innosoft.s24.a298; https://n2t.net/ark:/42411/s24/a298
• Nivel de acceso: acceso abierto
• Materia: Technical assistance; Artificial intelligence; Language modelling; IT support; Automated suggestions; Asistencia técnica; Inteligencia artificial; Modelos de lenguaje; Soporte TI; Sugerencias automatizadas

The proposal for a web application to assist in the management of technical incidents in the field of information technology is established. The implementation was carried out with a 3-layer architecture, based on web technologies using React, Laravel, and a relational database. Large language models were implemented, applying instruction design techniques to analyze descriptions of technical incidents and automatically provide suggestions and classify priority, based on criteria for incidents generated in the present. The proposal was developed based on the SCRUM agile methodology and validated with real users, who evaluated the functionality and accuracy of the system. The tool achieved a 77.3% accuracy in proposing correct suggestions, excelling in categories such as software and networks. These results demonstrated the usefulness of the solution as support in the selection of solutions and in reducing cognitive effort during the initial stages of diagnosis. It is concluded that the use of LLMs in technical support represents an effective alternative for optimizing processes, as long as it is used as a complement to human experience.