Nodal Prompting: rethinking authorial practice in Generative AI

Authors

DOI:

https://doi.org/10.54103/2531-5994/31893

Keywords:

generative AI, ComfyUI, Nodal prompting, prompting, human-computer interaction, agency, audiovisual production

Abstract

The growing integration of Generative AI tools in audiovisual production has turned prompting into the primary interface between human intent and machine output. However, existing theoretical frameworks treat the prompt as a discrete textual artifact, overlooking the increasingly complex interaction models enabled by node-based workflow environments such as ComfyUI, where generative instructions are distributed across interconnected parametric structures.

This article proposes the concept of nodal prompting, a mode of human-AI interaction intrinsic to graph-based generative tools, in which authorial intent is wired across a network of nodes, each contributing partial specifications that jointly determine the generative output.

The argument is developed through qualitative analysis of a production case study: Il partigiano Libero, an audiovisual series on a partisan during the Italian Resistance, developed by Motion Pixel and ANPI Chiomonte, and situated within a critical review of existing literature on prompt engineering, parametric authorship, and human-AI co-creation.

The analysis identifies a constitutive paradox at the core of nodal prompting: the author delegates expressive execution to the machine while simultaneously exercising greater upstream control through system design. This redistribution of creative agency challenges linear models of AI-assisted authorship and suggests that efficiency gains in generative production are conditional on significant investment in workflow architecture. Ultimately, the figure of the author is not dissolved but fundamentally restructured.

Downloads

Download data is not yet available.

References

Banh, L., & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets, 33(1), 63. https://doi.org/10.1007/s12525-023-00680-1 DOI: https://doi.org/10.1007/s12525-023-00680-1

Black Forest Labs. (2024). FLUX.1: A family of flow matching text-to-image models. https://bfl.ai/announcing-black-forest-labs/

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., . . . Amodei, D. (2020). Language models are few-shot learners. https://arxiv.org/abs/2005.14165

Bruno, G. (2025). Generative AI in cinema: A case study on the series “Il partigiano Libero” [Master’s Thesis]. Politecnico di Torino. Retrieved May 11, 2026, from https://webthesis. biblio.polito.it/35421/

Couldry, N. (2012). Media, society, world: Social theory and digital media practice. Polity.

Deng, S., Ye, Z., & He, J. (2025). An empirical study on game prop design: A node-based generative workflow in comfyui. 2025 IEEE/IEIE International Conference on Consumer Electronics-Asia (ICCE-Asia), 1–6. https://doi.org/10.1109/ICCE-Asia67487.2025.11263674 DOI: https://doi.org/10.1109/ICCE-Asia67487.2025.11263674

Goodfellow, P. (2024). The distributed authorship of art in the age of ai. Arts, 13(5), 149. https://doi.org/10.3390/arts13050149 DOI: https://doi.org/10.3390/arts13050149

Gunkel, D. J. (2025). Prompted by me. generated by chatgpt. Human-Machine Communication, 10(1), 2. https://doi.org/10.30658/hmc.10.2 DOI: https://doi.org/10.30658/hmc.10.2

Hong, W., Ding, M., Zheng, W., Liu, X., & Tang, J. (2023). CogVideo: Large-scale pretraining for text-to-video generation via transformers. International Conference on Learning Representations. https://arxiv.org/abs/2205.15868

Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2022). LoRA: Low-rank adaptation of large language models. International Conference on Learning Representations. https://arxiv.org/abs/2106.09685

Kyaw, A., & Sivalingam, K. (2025). Node-based editing for multimodal generation of text, audio, image, and video [NeurIPS 2025 Workshop on Generative and Protective AI for Content Creation]. https://doi.org/10.48550/arXiv.2511.03227

Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. Oxford DOI: https://doi.org/10.1093/oso/9780199256044.001.0001

university press.

Lian, L., Li, B., Yala, A., & Darrell, T. (2024). LLM-grounded diffusion: Enhancing prompt understanding of text-to-image diffusion models with large language models. Transactions on Machine Learning Research. https://arxiv.org/abs/2305.13655

Singh, A. (2023). A survey of ai text-to-image and ai text-to-video generators, 32–36. https://doi.org/10.1109/airc57904.2023.10303174 DOI: https://doi.org/10.1109/AIRC57904.2023.10303174

Van Dijck, J., Poell, T., & De Waal, M. (2018). The platform society: Public values in a connective world. Oxford university press. DOI: https://doi.org/10.1093/oso/9780190889760.001.0001

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2023). Chain-of-thought prompting elicits reasoning in large language models. https://arxiv.org/abs/2201.11903 DOI: https://doi.org/10.52202/068431-1800

White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., & Schmidt, D. C. (2023). A prompt pattern catalog to enhance prompt engineering with ChatGPT. https://arxiv.org/abs/2302.11382

Zhang, S., Wang, H., & Yi, X. (2025). Exploring collaboration patterns and strategies in human-ai co-creation through the lens of agency: A scoping review of the top-tier hci literature. Proc. ACM Hum.-Comput. Interact., 9(7). https://doi.org/10.1145/3757594 DOI: https://doi.org/10.1145/3757594

Yang, Z., Teng, J., Zheng, W., Ding, M., Huang, S., Xu, J., Yang, Y., Hong, W., Zhang, X., Feng, G., Yin, D., Zhang, Y., Wang, W., Cheng, Y., Xu, B., Gu, X., Dong, Y., & Tang, J. (2025). CogVideoX: Text-to-video diffusion models with an expert transformer. https://arxiv.org/abs/2408.06072

Downloads

Published

2026-08-06

How to Cite

Bruno, G., & Mazali, T. (2026). Nodal Prompting: rethinking authorial practice in Generative AI. DigitCult - Scientific Journal on Digital Cultures, 11(1), 84–100. https://doi.org/10.54103/2531-5994/31893

Issue

Section

Articles
Received 2026-06-17
Accepted 2026-07-16
Published 2026-08-06