Easy Tokens Question 3 of 223

What is tokenization?

GenAI / LLM · Speak this in 60–90 seconds · Faridabad & Delhi NCR

The model does not read letters like humans. It reads these pieces, then predicts the next one.

Tokenization converts raw text into a sequence of integer IDs from a fixed vocabulary.

LLMs use tokens (not full words) because it helps them:

Tokens questions separate people

who only read docs from people who shipped.

Keep it short, concrete,

and tied to GenAI / LLM work.

Stable token IDs

Each piece maps to a number the network can learn.

Fits the model

Fixed pieces are what transformers expect as input.

Before the model can read a sentence, it goes through these steps:

1
Tokenization converts raw text
into a sequence of integer IDs from a fixed vocabulary.
2
The model never sees
characters directly
3
Embeddings
it sees those IDs.
4
A matching detokenizer turns
generated IDs back into readable text.
5
Next token
The model scores what should come next.
6
Decode
IDs turn back into readable text.

Let's see how a real sentence is tokenized (tokens may vary by model):

Input text

“The model never sees characters directly”

Tokenized output

Themodelneverseescharactersdirectly

Token IDs (example)

2987408337471632900

Note: Actual tokens and IDs depend on the tokenizer (e.g., GPT, Llama, etc.).

Tokenization converts raw text into a sequence of integer IDs from a fixed vocabulary. The model never sees characters directly