Adapting a single prompt for different AI models primarily involves understanding each model's unique strengths and limitations. One must consider model-specific syntax and special tokens, as these can significantly influence interpretation. For large language models, adjusting the level of detail and explicit instructions is crucial; some models require more hand-holding while others excel with concise input. When adapting for generative image models, incorporating negative prompts and focusing on descriptive visual elements becomes paramount. Furthermore, understanding the model's typical output format and requesting it explicitly can prevent inconsistencies. Ultimately, an iterative refinement process by testing and adjusting the prompt's phrasing, structure, and intent is essential to achieve optimal results across diverse AI architectures. More details: https://abcname.net