Today’s topic might seem a bit technical, but don’t worry—we’re keeping it down-to-earth. Let’s uncover the secrets of tokens, the building blocks of AI’s understanding of language. If you’ve ever used ChatGPT or similar AI tools, you might have noticed something: when you ask a long question, it takes a bit longer to answer. But short questions? Boom, instant response. That’s all thanks to tokens. 1. What Are Tokens? A token is the smallest unit of language that AI models “understand.” It could be a sentence, a word, a single character, or even part of a word. In short, AI doesn’t understand human language—but it understands tokens. Take this sentence as an example: “AI is incredibly smart.” Depending on the tokenization method, this could be broken down into: Word-level tokens: ["AI", "is", "incredibly", "smart"] Character-level tokens: ["A", "I", " ", "i", "s", " ", "i", "n", "c", "r", "e", "d", "i", "b", "l", "y", " ", "s", "m", "a", "r", "t"] Subword-level tokens (the most common method): ["AI", "is", "incred", "ibly", "smart"] In a nutshell, AI breaks down sentences into manageable pieces to understand our language. Without tokens, AI is like a brain without neurons—completely clueless. 2. Why Are Tokens So Important? AI models aren’t magical—they rely on a logic of “predicting the next step.” Here’s the simplified workflow: you feed in a token, and the model starts “guessing” what’s next. It’s like texting a friend, saying “I’m feeling,” and your friend immediately replies, “tired.” Is it empathy? Nope—it’s just a logical guess based on past interactions. Why Does AI…