Tokenization is Just Like Chopping Wood

🪓 Tokenization: The Wood Log Analogy

You can’t drag a whole tree trunk into your workshop and instantly turn it into a table.
First, you grab your axe (or chainsaw if you’re fancy) and chop it into smaller, workable logs.

That’s exactly what tokenization does for text.
It takes a giant block of text and slices it into smaller units called tokens.

These tokens might be:

• A whole word

• Part of a word

• Even punctuation marks

How small you chop depends on the job — just like woodwork, different projects need different cuts.

Once you’ve got those smaller pieces, you can start shaping and arranging them into something useful.
That’s where vector embeddings come in — turning tokens into numbers so a computer can actually process them.

If you want to see that step, check out my post: Vector Embedding for All.

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High-Level Diagram

flowchart LR
    A[Long Wooden Log 🪵] -->|Chop into pieces| B[Firewood Logs 🪓]
    B -->|Burn in fireplace| C[Warm Fire 🔥]

    subgraph Analogy
    A
    B
    C
    end

    X[Long Text 📜] -->|Tokenize| Y[Tokens 🔤]
    Y -->|Convert to numbers| Z[Machine-Readable Form 🔢]

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Takeaway

Tokenization is the chopping stage.
You start with something big — a paragraph, sentence, or document — and split it into smaller, meaningful parts.
Those tokens are the building blocks that computers can transform into something they can work with.