LARGE LANGUAGE MODELS FOR GENERATING CREATIVE CONCEPTS IN VISUAL ART PRE-PRODUCTION PROCESSES
DOI:
https://doi.org/10.29121/shodhkosh.v7.i4s.2026.7502Keywords:
Large Language Models, Computational Creativity, Visual Art Pre-Production, Concept Generation, Human–AI Collaboration, Prompt Engineering, Creative AI, Digital Art WorkflowAbstract [English]
Pre-production phase of visual art is a very significant stage because it entails intellectual ideation, story development and search of design. The need to possess smart looking systems that can be utilized to augment the traditional ideation work is growing as well as requirements of fast and diverse creative effort are escalating. The article dwells upon the application of Large Language Models (LLMs) to generate creative concepts in pre-production in visual art. With their abilities to manipulate and generate semantically rich textual data, LLCs are in a good position to be utilized to assist in supporting the early-stage artistic processes. The study proposes a formal methodology that would involve timely engineering, notion generation, and evaluation into a human-AI work system. System architecture is a developed system that assists in the conversion of user specified inputs to structured creative concepts like design of characters, descriptions of scenes and thematic scripts. The paper also explains how LLC can be incorporated with digital art tools in such a way that the ideation process through text may be incorporated into a visual representation without interruption. The obtained outcomes of the experiment show that the workflows that are assisted by LLM have a positive influence on the diversity, originality, and the quality of idea generation as compared to the traditional methods of idea generation. The generated concepts are evaluated using a detailed evaluation framework to assess the quality of the concepts generated by using various measures such as coherence, relevance, aesthetic potential and diversity. In addition, the user study, which will be carried out with artists and designers, will assist in receiving the concept of the practical applicability and usability of the offered approach. The findings demonstrate that LLMs can be regarded as efficient co-creative partners that help users overcome the issue of creative paralysis and expand the scope of their conceptual exploration without losing their artistic control. Despite these advantages, the originality, bias and creative evaluation problems are still present, which proves the need of more research. The discussion of the future directions, including multimodal integration, personalization of AI tools, and the development of the standardized ways of creativity measurement, conclude the paper. Overall, the work is applicable to the field of computational creativity as it demonstrates the possibility of using LLM to enhance the pre-production process related to the visual art and rebrand the human-AI collaboration in the creative industries.
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