In our latest cohort, we tackled a similar challenge of integrating real-time variables into generative AI models — though in a different domain. A critical insight we found was the importance of a modular architecture. By decoupling the AI's core styling logic from the bid-aware components, you create a system that's flexible and easier to maintain. For developers, this means implementing a two-layer model architecture: a base generative model for fashion recommendations and a secondary module that adjusts these recommendations based on auction dynamics. You can think of this secondary module as a filter or transformer that refines outputs from your baseline model in accordance with real-time bid data. One practical framework we used is the Transformer-based architecture, which is highly effective for real-time data processing. The key is feeding the real-time auction variables into the model as attention layers, allowing the system to weigh these variables appropriately in its decision-making process. Also, consider leveraging Reinforcement Learning (RL) to continuously improve the model's performance based on feedback from user interactions and auction outcomes. By implementing a reward system that prioritizes profitable outcomes, your model can autonomously learn optimal strategies for bid-aware styling. Combining these approaches can significantly enhance your AI's ability to generate profitable fashion recommendations that align with auction dynamics. If you're looking