Evolution of Embedding Models and RAG

This chapter explores the development and efficacy of embedding models, starting with Word2Vec, and their role in semantic search with minimal fine-tuning. It emphasizes the advantages of Retrieval-Augmented Generation (RAG) over traditional fine-tuning methods and outlines the complexities of optimizing data management for AI performance. The discussion also covers future challenges for vector databases, including scalability and security, along with the importance of embedding technology in enhancing search efficiency.