The Generative Feedback Loop (GFL) is an approach to optimize and improve the outputs of Large Language Models (LLMs) like GPT. It involves creating a dynamic cycle that adapts LLMs to new and continuously changing data, and user needs. This can be achieved through personalized recommendations, targeted ads, or identifying trends, where an AI might suggest products to a user based on their browsing history, clicks, or purchases. The feedback loop provides benefits such as being dynamic, personalized, and scalable, allowing the model to improve its output without manual intervention. In this article, a sample solution is presented that suggests podcasts to users based on their listening history, using GFL to create a feedback loop that learns user profiles and finds out what they love to listen to. The project uses Azure Serverless Functions in Python to handle real-time requests, stores data in the Neon database, retrieves user listening history, generates vector embeddings for available podcasts, and provides personalized podcast suggestions.