Personalised healthy food text recommendations through fuzzy linguistic variables: A generative AI-based approach Gutiérrez Batista, Karel Martín Bautista, María José Morcillo Jiménez, Roberto Morales Garzón, Andrea Rojas Carvajal, Ana María Food recommendation Generative models Prompt engineering Natural language processing Fuzzy logic This research was partially supported by the Grant PID2021-123960OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe. It was also funded by “Consejería de Transformación Económica, Industria, Conocimiento y Universidades de la Junta de Andalucía” through a pre-doctoral fellowship program (Grant Ref. PREDOC_00298). In addition, this research has been partially supported by the project CITIC-2024-06, funded by the Research Center for Information and Communication technologies of the University of Granada. Funding for open access charge: Universidad de Granada/CBUA. Nutrition and healthy eating habits are fundamental for the global population. Nowadays, there is an increasing tendency to consume less healthy recipes and a low general knowledge of nutrition. In these terms, generative AI arises as a potential tool for health-aware food recommendations, especially when improving communication with the user. This study presents a pipeline to enrich prompts with fuzzy modelling to increase the quality of textual recommendations. We apply our pipeline to generate a personalised frequency of food consumption, considering both nutritional and individual profiles. This is an essential task for increasing the health-conscious recommendation systems. We conducted extensive experimentation across different roles and prompt strategies. We evaluated the quality of the text and the nutritional rigour of the text responses. Our results show that enriching prompts with fuzzy modelling of the nutritional information of the foods significantly improves the quality of the prompt responses. 2025-06-02T11:54:51Z 2025-06-02T11:54:51Z 2025-05-12 journal article Morales-Garzón, A., Carvajal, A. M. R., Morcillo-Jimenez, R., Martin-Bautista, M. J., & Gutiérrez-Batista, K. (2025). Personalised healthy food text recommendations through fuzzy linguistic variables: A generative AI-based approach. Applied Soft Computing, 113234. https://doi.org/10.1016/j.asoc.2025.113234 https://hdl.handle.net/10481/104425 10.1016/j.asoc.2025.113234 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier