LLM Tokens: What They Are, How They Work, and Why They Matter

When you type a question into an AI chatbot, it doesn’t see words—it sees LLM tokens, discrete units of text that large language models use to process and generate language. Also known as text fragments, these tokens are the raw material AI works with, whether you’re asking for a recipe or analyzing a legal contract. Every word you type gets broken down into these chunks—sometimes one token per word, sometimes split into pieces like "un" and "happy"—and the model builds meaning from that sequence.

Tokenization isn’t just a technical step; it directly affects how well your AI performs. A model with a vocabulary size, the total number of unique tokens a model can recognize that’s too small will struggle with rare words or technical terms. Too large, and it wastes memory, slows down responses, and increases costs. That’s why tokenization, the method used to split text into tokens, often using techniques like Byte Pair Encoding is one of the most underrated levers in AI design. Companies like OpenAI and Meta tweak this daily to balance speed, accuracy, and budget.

And it’s not just about words. Tokens determine how much your AI remembers. Each token you send to a model requires storage in its KV cache, a memory structure that holds past tokens to help the model maintain context. Long conversations? More tokens. More tokens? Higher memory use. That’s why optimizing token usage isn’t just about saving money—it’s about making AI feel faster and more responsive. If your AI keeps pausing or cutting off mid-sentence, it’s often because the system ran out of token space.

What you’ll find below are real, practical deep dives into how tokens shape everything: from why smaller models can still reason well (thanks to clever token reuse), to how hallucinated citations happen because of token misalignment, to why your AI might be wasting 40% of its compute on unnecessary tokens. These aren’t theory pieces—they’re guides from teams who fixed latency, cut costs, and improved accuracy by tuning token handling. Whether you’re building AI tools, using them for research, or just trying to get better answers, understanding tokens isn’t optional. It’s the foundation.