The digital and intelligent development of aero engines has led to an increasing number of modeling and code development tasks. With the advancement of generative artificial intelligence technology, products such as ChatGPT and Copilot have achieved significant results in the fields of text generation and code writing. However, in professional modeling scenarios, owing to the high skill requirement for the prompt interaction process, the generated code often fails to meet the expected computational requirements. Therefore, auxiliary generation technology for model code on the basis of prompt engineering, which is based on software requirement specifications, has been used to design a prompt framework and process for the field of aero engine modeling by effectively guiding the generative model to generate code snippets that meet specific requirements. The application of this technology helps shorten the code development cycle and improve modeling efficiency, thereby promoting the development of engine digitization and intelligence. This chapter uses an engine fuel system as a typical case to show the specific application methods of the prompt framework and process for code generation.