Implementing RAG for micro-targeting based on customer data
Having thoroughly analyzed the dataset and constructed a robust retrieval system, we now transition from theoretical frameworks to practical implementation. In this section, we will learn how to apply RAG to dynamically address common challenges in digital marketing, such as outdated information in trained models and capturing recent user interactions. Traditional zero-shot learning (ZSL) and few-shot learning (FSL) models, while powerful, often lag in real-time responsiveness and rely on pre-existing data, limiting their effectiveness in such a fast-paced marketing scenario.
To overcome these limitations, we will utilize RAG to generate marketing content that is not only up to date but also deeply relevant to current consumer behaviors. By integrating our retrieval system with GPT, we can pull the latest user interaction data directly from our database. With RAG, we can also generate real-time content tailored to individual...
