Easy Tokens Question 1 of 223

What is a token in an LLM?

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

PICTURE THIS: A SENTENCE BECOMES TOKENS

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

Simple meaning

A token is a chunk of text the model reads and writes, often a subword rather than a full word.

WHY — Tokens instead of words?

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.

STEPS — What happens with tokens?

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

1
A token is a
chunk of text the model reads and writes, often a subword rather than a full word.
2
Tokenizers split input into
these units before the network runs.
3
Billing, context limits, and
generation length are all counted in tokens, not characters or words.
4
Context mix
Attention looks at nearby tokens together.
5
Next token
The model scores what should come next.
6
Decode
IDs turn back into readable text.

EXAMPLE — See it in action

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

Input text

“Tokenizers split input into these units before the network runs.”

Tokenized output

Tokenizerssplitinputintotheseunits

Token IDs (example)

2987408337471632900

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

Key takeaway

A token is a chunk of text the model reads and writes, often a subword rather than a full word. Tokenizers split input into these units before the network runs.