Web application for classifying and assisting in incident management using OpenAI LLMs
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 model...
| Autores: | , , , |
|---|---|
| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2025 |
| País: | Perú |
| Recursos: | Universidad La Salle |
| Repositório: | Revistas - Universidad La Salle |
| Idioma: | espanhol |
| OAI Identifier: | oai:ojs.revistas.ulasalle.edu.pe:article/298 |
| Acesso em linha: | https://revistas.ulasalle.edu.pe/innosoft/article/view/298 https://doi.org/10.48168/innosoft.s24.a298 https://n2t.net/ark:/42411/s24/a298 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Technical assistance Artificial intelligence Language modelling IT support Automated suggestions Asistencia técnica Inteligencia artificial Modelos de lenguaje Soporte TI Sugerencias automatizadas |
| Resumo: | 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. |
|---|