Summary

This chapter aimed to develop a scaled RAG-driven generative AI recommendation system using a Pinecone index and OpenAI models tailored to mitigate bank customer churn. Using a Kaggle dataset, we demonstrated the process of identifying and addressing factors leading to customer dissatisfaction and account closures. Our approach involved three key pipelines.

When building Pipeline 1, we streamlined the dataset by removing non-essential columns, reducing both data complexity and storage costs. Through EDA, we discovered a strong correlation between customer complaints and account closures, which a k-means clustering model further validated. We then designed Pipeline 2 to prepare our RAG-driven system to generate personalized recommendations. We processed data chunks with an OpenAI model, embedding these into a Pinecone index. Pinecone’s consistent upsert capabilities ensured efficient data handling, regardless of volume. Finally, we built Pipeline 3 to leverage over...