Explaining Tokenization to a Fresher

When you first hear about GPT, it sounds like magic — you type something, and it replies like it actually understands you. But under the hood, there’s a very important first step happening before GPT can even begin thinking about your question. That step is called tokenization.

Think of GPT as a super-smart text generator. It can write stories, answer questions, explain concepts, and even help you debug code. But here’s the thing — GPT doesn’t actually “see” sentences the way you and I do. It can’t just take your entire paragraph and immediately understand it.

Instead, when you feed GPT some text, the first thing it does is break that text into smaller pieces called tokens. These tokens could be entire words, parts of words, or sometimes even just single characters. For example:

• “Elephant” might become just one token.
• “Unbelievable” might be split into “un”, “believ”, and “able”.
• Common words like “a” or “the” are their own tokens.

Why does it do this? Because breaking text into tokens makes it easier for the model to understand and process language consistently. It doesn’t have to know every single possible word in existence — it just needs to know how to work with these smaller building blocks.

Once your text is tokenized, GPT turns each token into a set of numbers (this is where vector embeddings come in). These numbers represent meaning in a way the model can understand. From there, GPT predicts the next token, then the next, and so on until it forms a complete answer.

So in simple terms: tokenization is like chopping your text into puzzle pieces that GPT can actually work with. Without this step, GPT wouldn’t be able to process your words at all — it would just see a giant blob of text and be completely lost.