OpenAI Tokenization Explained: Why Words Split into Multiple Tokens

Description

In this video, we explore how OpenAI tokenizers break down text — showing why models like GPT-3.5 and GPT-4 often split long or complex words into multiple tokens. You’ll learn how subword tokenization helps models understand new words efficiently, keeps vocabulary sizes small, and reduces overall model parameters — all while improving generalization. We’ll also demonstrate how to use OpenAI’s tiktoken library to inspect tokenization, estimate costs, and count tokens in your prompts before sending them to the API. You'll learn how to: - Understand how GPT tokenizers split text into subwords - See why prefixes and suffixes (like “un-”, “re-”, “-tion”) become separate tokens - Explore how subword tokenization reduces vocabulary and model size - Compare examples using OpenAI’s online tokenizer tool - Use the `tiktoken` Python library to count tokens and estimate costs Watch this video if you want to understand how GPT tokenization works under the hood — or if you’re optimizing prompt length, cost, or fine-tuning efficiency. This video is part of the LLM Engineering & Deployment Certification Program by Ready Tensor. ✅ Enroll Now: https://app.readytensor.ai/certifications/llm-engineering-and-deployment-DAROCXlj About Ready Tensor: Ready Tensor helps AI/ML professionals build and evaluate intelligent, goal-driven systems and showcase them through certifications, competitions, and real-world project publications. 🌐 Learn more: https://www.readytensor.ai 👍 Like the video? Subscribe and let us know what other LLM engineering concepts you’d like us to cover next!