A deep dive into tokenizers, the invisible first piece of your LLM stack. Learn how they control costs, context windows, and performance, and see how algorithms like BPE and SentencePiece can make or break your AI.

Osman's Odyssey: Byte & Build

Tokenizers are the critical first step in LLM processing, converting text into numerical tokens that models can understand. Different algorithms like BPE, WordPiece, and SentencePiece each have trade-offs affecting vocabulary size, memory usage, and multilingual support. The choice of tokenizer impacts prompt costs, context windows, training speed, and model performance, making it a foundational decision that shapes the entire AI pipeline.

First Came The Tokenizer : Why the Humble Tokenizer Is Where It All Starts · Osman's Odyssey: Byte & Build

Why the Humble Tokenizer Is Where It All Starts

Tokenizers: The Hidden Operators Behind LLMs

From Whitespace to Subwords: A Lightning Tour

Tokenizer Quirks: Fun Ways To Sabotage Yourself

Picking a Tokenizer for Your LLM Playground: My Cheat Sheet

Final Words: The Humble Tokenizer Is Doing More Than You Think