Exploring Foundations of Large Language Models (LLMs): Tokenization and Embeddings

Have you ever wondered how various Gen AI tools like ChatGPT or Bard efficiently answer all our complicated questions? What goes behind the scenes to process our question and generate a human-like response with the size of data in magnitudes? Let’s dive deep. In the era of Generative AI, natural language processing plays a crucial role in how machines understand and generate human language. The applications for this cut through various implementations like smart chatbots, translation, sentimental analysis, developing knowledge basis, and many more. The central theme in implementing this Gen AI application is to store the data from various sources and query those to generate human language responses. But how does this work internally? In this article, we will explore concepts of tokenization and embeddings, which play a vital role in understanding human queries and converting knowledge bases to generate responses.