<ul><li>Generative AI application stores data from multiple sources, and queries the data to generate human language responses. </li><li>Tokenization is the process of breaking down human text into smaller units assigned with token IDs that can be processed by models. </li><li>There are various types of tokenization like sentence-level, word-level, subword, character-level, and hybrid tokenizer to optimize efficiency and flexibility. </li><li>Embedding is the process of converting tokenized text to numerical format, enabling the model to process it. </li><li>There are three known types of embeddings: traditional word embeddings, contextual embeddings, and positional embeddings. </li><li>To generate human-like outputs, user query vectors are compared with corpus vectors through cosine similarity or Euclidean distance. </li><li>Tokenization and embeddings are two critical processes that enable LLMs to process and generate human language. </li><li>As GenAI evolves, improving tokenization methods and embeddings will be important to build more efficient and powerful language models. </li></ul>

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

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