This study explores the application of Generative Artificial Intelligence (GAI) in addressing problem-solving tasks within the field of economics. Its primary objective is to generate executable code capable of solving specific problems in this domain. To this end, the methodology centers on prompt engineering, which is employed as a means of articulating problems in terms of software requirements specifications tailored for subsequent development. The introduction provides an overview of the aims and capabilities of GAI, offering a contextual framework for the investigation. A review of the state of the art is presented to position this research within existing literature. The methodology section details the process for designing prompts that serve as inputs to the GAI system. Following this, a series of illustrative examples drawn from operations research and financial mathematics are examined. Each case includes a description of the prompt formulation, the code generated by the GAI model, and the execution results, thereby demonstrating the problem-solving process. The findings indicate that the proposed problems can be effectively addressed using this approach. Moreover, the generated solutions enable the construction of computational models that support validation and analysis. Overall, the proposed technological and methodological framework shows strong potential for tackling challenges in financial and mathematical contexts, delivering promising and robust results.