Introduction to RAG for precision marketing

Generative models, particularly those developed on transformer frameworks like generative pre-trained transformer (GPT), have revolutionized how machines understand and generate human-like text. These models are trained on vast corpora of text data and are capable of learning complex patterns and structures of language that enable them to predict and generate coherent and contextually appropriate text sequences. However, despite their sophistication, pure generative models often lack the ability to incorporate real-time, specific information that isn’t explicitly present in their training data.

This is where the “retrieval” component of RAG comes into play. RAG is a fusion of Generative AI (GenAI) with information retrieval systems, forming a hybrid model designed to enhance the quality and relevance of generated content. RAG achieves this by incorporating a dynamic retrieval component that pulls relevant information...