## 8 Discussion
### How to Use This Work  
This paper provides a comprehensive framework and set of methodologies designed to support the effective use of AI in visualisation design. Below, we outline key ways in which different readers, including researchers, designers, and developers, can benefit from and apply the content presented here.  
* Researchers in the field of AI and data visualisation can leverage the five-part framework presented in this paper to extend existing models or explore new avenues in prompt engineering, design generation, and visualisation aesthetics. The discussions around design vocabularies, corpus analysis, and the application of visualisation principles offer a foundation for further study and experimentation. The analysis of corpora and frequency-based term investigation may serve as a springboard for a more in-depth analysis of visualisation trends across different domains. The five-part framework (subject, aesthetics, context, application, and priorities) offers a structured approach to prompt engineering for visualisation, helping users fine-tune AI models to generate tailored visuals.
* Designers working with AI-generated visualisations will find practical guidelines and useful design references to guide their process. By following the main five-part framework, along with the secondary and tertiary categories, they will gain a comprehensive understanding of the various aspects of visualisation design. The provided vocabulary can be directly applied to inspire and develop new generative ideas.
* AI developers and engineers can use this paper to improve their models' generative capabilities by focusing on the core concepts of weighting terms, understanding contextual influences, and balancing between artistic and functional requirements in visual outputs. The insights into prompt engineering, alongside the use of specialised corpora, provide developers with the tools to enhance both back-end processes (e.g., data manipulation) and front-end visual outputs (e.g., clarity and aesthetic balance).
* We also provide tables summarising the frequency analysis and collocated terms to provide quick reference points for readers to identify commonly used terms and their associated contexts, whether designing for exploratory visualisation, dashboards, or other applications. These sections offer practical tools that can be applied in real time when using generative AI systems to create visual outputs.
* This paper includes practical examples and case studies demonstrating how the proposed methods can be applied in different scenarios. Readers can use these examples as templates for similar use cases or adapt them for more specific needs.  
Future research will refine the VisAlchemy tool and expand its use across various contexts, including a usage study on participants using the tool. We plan to build upon the Large Language Model, making it more of a visualisation-specific AI model, which may perform better on complex design briefs. However, using the current, generally trained models, we enable more diverse image suggestions and consider that a specifically trained one may be too focused or convergent to current design ideas. Our plans also include investigating different interface layouts and integrating them with a design process, such as the Five Design Sheets method. We aim to enhance the framework's ability to produce inspirational visuals and explore its educational benefits by incorporating it into design education, thereby equipping future designers with AI-driven creative skills. The VisAlchemy tool and five-part framework are promising developments in visualisation design, aiming to foster creativity and AI collaboration. Continued exploration and refinement will yield significant insights and innovations. Our research opens new avenues for exploring how AI can contribute to creative processes beyond visualisation design. The framework's principles may apply to other design domains, suggesting a broader impact on creative industries.