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Author Topic: The Tokenisation Pipeline Re-exaplained  (Read 25 times)

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The Tokenisation Pipeline Re-exaplained
« on: Yesterday at 03:13:20 PM »
Because this is so critical to understand, I have revisited it again, and this is easier to understand

Tokens are just numbers but your human input/queries are words, so:

Neural networks can only work with numbers, so text must be converted into numeric form before the model can process it.A neural network doesn’t “think.”

It has billions of weights — tiny numbers — that were adjusted during training so that it can predict the next token in a sequence. It's intelligent in a human sense.

It’s a giant statistical machine that has learned patterns from an enormous amount of text.When you ask it something, it outputs the most likely next tokens based on those learned patterns.

That’s the essence.

🔧 Tokens are numbers → Neural networks operate only on numbers → Weights/biases are just parameters → Training adjusts those parameters using huge datasets → The model predicts the most likely next token.

🔍 “Tokens are not numbers but are are IDs (integers).

Those IDs are mapped to vectors (embeddings).

The vectors are what the neural network actually consumes.

So the pipeline is: Text → tokens → embeddings → neural network.

 “It just spits out the most likely results but better phrasing is: "It predicts the next token by computing a probability distribution over all possible tokens, then choosing one according to that distribution". It's not deterministic unless forced to be.

 “It’s not really intelligent” — but I
It behaves intelligently because the patterns it learned are extremely rich, but it does not understand, reason, or have goals.

🧠 Summary:

An AI model doesn’t understand language. It only understands numbers.

So text is broken into tokens (numbers), which are turned into vectors that a neural network can process.

The neural network has billions of tiny numeric parameters that were tuned during training on massive amounts of text.

When you ask it something, it predicts the most likely next token based on the patterns it learned. It's not thinking — it’s doing extremely advanced autocomplete or think of it as massively accurate predictive text.

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measure twice, cut once

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