Easy Transformers Question 12 of 223

What problem did transformers solve compared with RNNs?

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

RNNs process tokens one after another, which is slow to train and weak on long-range links.

WHY — Tokens instead of words?

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

Transformers 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
RNNs process tokens one
after another, which is slow to train and weak on long-range links.
2
Transformers attend over the
whole sequence at once, so training parallelizes on GPUs.
3
That scaling path made
today's LLMs practical.
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

“Transformers attend over the whole sequence at once, so training parallelizes on”

Tokenized output

Transformersattendoverthewholesequence

Token IDs (example)

2987408337471632900

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

Key takeaway

RNNs process tokens one after another, which is slow to train and weak on long-range links. Transformers attend over the whole sequence at once, so training parallelizes on GPUs.