ChatGPT is a versatile conversational Artificial Intelligence model that responds to user input prompts, with applications in academia and various sectors. However, crafting effective prompts can be challenging, leading to potentially inaccurate or contextually inappropriate responses, emphasizing the importance of prompt engineering in achieving accurate outcomes across different domains. This study aims to address this void by introducing a methodology for optimizing interactions with Artificial Intelligence language models, like ChatGPT, through prompts in the field of engineering. The approach is called GPEI and relies on the latest advancements in this area; and consists of four steps: define the objective, design the prompt, evaluate the response, and iterate. Our proposal involves two key aspects: data inclusion in prompt design for engineering applications and the integration of Explainable Artificial Intelligence principles to assess responses, enhancing transparency. It combines insights from various methodologies to address issues like hallucinations, emphasizing iterative prompt refinement techniques like posing opposing questions and using specific patterns for improvement. This methodology could improve prompt precision and utility in engineering.

El objetivo del artículo es presentar los resultados de un análisis bibliométrico en el campo del modelado de la difusión de innovaciones que permita realizar un diagnóstico de la evolución y estado actual del tema. La metodología empleada consiste en la construcción y posterior análisis de los indicadores bibliométricos de cantidad y calidad, así como un análisis de la estructura topológica de las redes de autores atendiendo a su evolución temporal en el intervalo 2005- 2011. Como resultado, se encuentra un campo en continuo crecimiento y de gran dispersión, con alta dinámica en la evolución de sus redes.