In this episode of Swetlana AI Podcast, we explore the fascinating world of tokenization in AI, a process fundamental to how large language models (LLMs) like those from OpenAI function.

What are tokens, and why do they matter in AI?

We’ll break down the basics, from defining tokens to discussing how text is transformed into units that an AI model can process.

Whether you're familiar with AI or just starting out, understanding tokenization will help you better grasp how natural language processing (NLP) systems operate behind the scenes.

We'll dive into the different tokenization techniques used in LLMs, including word-level, character-level, and subword-level tokenization. You'll learn about Byte Pair Encoding (BPE), a method widely employed by models like OpenAI's, and how it impacts the performance and accuracy of AI when handling tasks like prompt compression. We’ll also touch on how tokenization is crucial for working with multimodal systems, where text interacts with other data forms like images and audio.

Tune in as we also discuss the broader implications of tokenization in AI, such as its role in shaping model efficiency. Whether you're curious about AI tokens or simply want to understand more about how text is tokenized in language models, this episode offers a comprehensive look at one of the key processes driving modern AI.

Podcast made with NotebookLM.

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