Tokenization
In this chapter, you'll dive into the fascinating world of tokens—those mysterious units that AI models like GPT use to process text. Understanding how these tokens work is crucial for developers as it directly affects costs and application design.
A More Detailed Look
Here, we explore how natural language turns into numerical data through tokenization. We break down sentences into smaller pieces called tokens, which are then transformed into word vectors or embeddings. This chapter uses the OpenAI Tokenizer to illustrate this process with examples like "How to build my own AI?" and variations thereof.
What You Need to Know
Token Count
Discover why predicting exact token counts is tricky due to the complex relationship between human language and tokens. Learn how to estimate costs for your applications based on these unpredictable counts.
Word Vector Encoders
Explore various word vector encoders, each with unique characteristics that determine their suitability for different tasks. Understand why mixing vectors from different sources can lead to inaccurate results.
Terminology
Navigate the often confusing terminology in AI by clarifying key terms like tokens and embeddings. See how these concepts fit together in a typical API workflow.
More on Word Embedding
Dive deeper into advanced topics such as BERT, Word2Vec, GloVe, and FastText. These powerful tools offer state-of-the-art performance for embedding words and can significantly enhance your AI applications.
This chapter is packed with insights that will help you build more efficient and cost-effective AI solutions. Ready to unlock the secrets of tokenization? Let's get started!