Start With The Big Picture
Every major AI model you've heard of:
ChatGPT ✓ Transformer
Claude ✓ Transformer
Gemini ✓ Transformer
LLaMA ✓ Transformer
Copilot ✓ Transformer
They all use the same fundamental architecture — the Transformer.
It was invented in 2017 by researchers at Google in a paper called "Attention Is All You Need." That one paper changed the entire field of AI.
Before Transformers — language models were slow, struggled with long text, and couldn't understand context well.
After Transformers — everything exploded. GPT, BERT, Claude, Gemini — all of it became possible because of this one architecture.
So what makes it so special?
The Problem It Was Solving
Before Transformers, the most popular way to process language was something called an RNN — Recurrent Neural Network.
An RNN reads text like a human reads a book — one word at a time, left to right.
"The cat sat on the mat"
RNN reads:
"The" → processes → remembers something
"cat" → processes → updates memory
"sat" → processes → updates memory
"on" → processes → updates memory
"the" → processes → updates memory
"mat" → processes → done
This had two serious problems:
Problem 1 — It forgot things from the beginning
By the time the RNN reached the end of a long sentence or paragraph, it had already mostly forgotten what was at the beginning. Like trying to remember the first sentence of a page after reading the whole page — the early stuff fades.
Problem 2 — It was slow
Because it processed words one by one in sequence — you couldn't speed it up. Word 5 had to wait for word 4. Word 4 had to wait for word 3. No way to process in parallel.
The Transformer solved BOTH of these problems completely.
The Transformer's Big Idea
Instead of reading text word by word — the Transformer looks at all words at the same time.
RNN approach:
"The" → "cat" → "sat" → "on" → "the" → "mat"
(one at a time, sequential)
Transformer approach:
"The" "cat" "sat" "on" "the" "mat"
↕ ↕ ↕ ↕ ↕ ↕
Every word looks at every other word simultaneously
Every single word looks at every other word at the same time and asks:
"How much should I pay attention to you right now?"
This is called the Attention Mechanism — and it's so important it gets its own module next (Module 2.4).
For now just understand — the Transformer's superpower is processing all words in parallel, while understanding how every word relates to every other word.
The Transformer — Inside the Box
Let's open it up and see what's inside.
The Transformer has two main parts:
┌─────────────────────────────────────┐
│ TRANSFORMER │
│ │
│ ┌─────────────┐ ┌──────────────┐ │
│ │ ENCODER │ │ DECODER │ │
│ │ │ │ │ │
│ │ Reads and │ │ Generates │ │
│ │ understands │ │ output │ │
│ │ the input │ │ token by │ │
│ │ │ │ token │ │
│ └─────────────┘ └──────────────┘ │
└─────────────────────────────────────┘
Encoder — reads your input and builds a deep understanding of it.
Decoder — takes that understanding and generates the output, one token at a time.
Different models use different parts:
GPT (ChatGPT) → Decoder only
→ Great at generating text
→ This is what LLMs use
BERT (Google) → Encoder only
→ Great at understanding text
→ Used for search, classification
T5, original → Encoder + Decoder
Transformer → Great at translation tasks
Since we're focused on LLMs — we care most about the Decoder.
What Happens Inside One Transformer Layer
The Transformer is not one thing — it's many identical layers stacked on top of each other.
GPT-3 has 96 layers. Each layer does the same type of processing, but learns different patterns.
Here's what happens inside one layer — simply:
Input tokens (as embeddings)
↓
┌─────────────────────────┐
│ ATTENTION │
│ │
│ Every token looks at │
│ every other token and │
│ figures out which │
│ ones matter most │
└─────────────────────────┘
↓
┌─────────────────────────┐
│ FEED FORWARD │
│ NETWORK │
│ │
│ Each token gets │
│ processed through │
│ a small neural │
│ network individually │
└─────────────────────────┘
↓
Richer, more meaningful
token representations
Then this output feeds into the NEXT layer. And the next. And the next.
Each layer builds a deeper understanding of the text.
A Real Life Analogy — The Team Meeting
Imagine a team of 10 people in a meeting, each person representing one word in a sentence.
The old way (RNN):
Person 1 speaks. Then passes a note to Person 2. Person 2 reads the note, speaks, passes a note to Person 3. And so on. By the time Person 10 gets the note — it's been rewritten 9 times. The original message is barely there.
The Transformer way:
All 10 people sit in a circle. Everyone can see everyone else. Before anyone speaks — everyone looks around the room and decides:
"Who in this room is most relevant to what I need to say?"
Person 1 (the word "bank") looks around:
- Sees Person 3 said "river" → pays HIGH attention to them
- Sees Person 7 said "money" → pays LOW attention to them
- Now Person 1 knows — in this sentence, "bank" means river bank
Everyone has full context. Nobody is waiting. Everything happens at the same time.
That's the Transformer.
The Three Steps — Input to Output
Let's trace your actual message through a Transformer LLM step by step:
Step 1 — Input Preparation
You type: "What is the capital of France?"
Tokenizer splits it:
["What", " is", " the", " capital", " of", " France", "?"]
Each token gets its embedding (list of 1536 numbers):
"What" → [0.2, 0.8, 0.1, ...]
" is" → [0.5, 0.3, 0.7, ...]
" the" → [0.1, 0.9, 0.2, ...]
" capital"→ [0.8, 0.4, 0.6, ...]
" of" → [0.3, 0.2, 0.8, ...]
" France" → [0.9, 0.7, 0.3, ...]
"?" → [0.1, 0.1, 0.9, ...]
Step 2 — Positional Encoding
Wait — there's a problem.
The Transformer processes all tokens at the same time. But order matters in language.
"Dog bites man" ≠ "Man bites dog"
Same words, completely different meaning. The Transformer needs to know the order.
So before processing, we add positional encoding — extra numbers added to each embedding that tell the model:
"What" → embedding + "I am token 1"
" is" → embedding + "I am token 2"
" the" → embedding + "I am token 3"
...
Now the model knows both the meaning AND the position of each token.
Step 3 — Through the Layers
The embeddings (with position info) flow through all the Transformer layers.
Layer 1: Basic patterns — grammar, word types
Layer 2: Slightly deeper — phrases, simple relationships
Layer 3: Deeper — sentence structure
...
Layer 96: Very deep — complex reasoning, context, meaning
Each layer transforms the embeddings into richer, more meaningful representations.
By the final layer — the model has a very deep understanding of your question.
Step 4 — Output Generation
After the final layer — the model produces a probability distribution:
What comes next after "The capital of France is"?
"Paris" → 94%
"Lyon" → 2%
"London" → 1%
"Berlin" → 0.5%
...
Picks "Paris." Adds it. Runs through all layers again. Picks the next token. And so on.
"The" → "capital" → "of" → "France" → "is" → "Paris" → "."
Why So Many Layers?
Think about how you understand a sentence.
"The trophy didn't fit in the suitcase because it was too big."
What does "it" refer to — the trophy or the suitcase?
To answer this you need to:
- Identify all the nouns (trophy, suitcase)
- Understand "fit" implies a size comparison
- Understand "too big" applies to whatever didn't fit
- Conclude "it" = the trophy
That's multiple levels of reasoning — each building on the previous.
Transformer layers work the same way:
Early layers → simple patterns (this is a noun, this is a verb)
Middle layers → relationships (this noun is the subject)
Later layers → complex reasoning (what does "it" refer to?)
More layers = ability to handle more complex language.
How Big is a Transformer Actually?
Let's put this in perspective:
GPT-2 (2019)
→ 48 layers
→ 1.5 billion parameters
→ Could write decent paragraphs
GPT-3 (2020)
→ 96 layers
→ 175 billion parameters
→ Could write essays, answer questions, write code
GPT-4 (2023)
→ Exact details not public
→ Estimated ~1 trillion parameters
→ Near human-level on many tasks
More layers and parameters = more capacity to learn complex patterns = better language understanding.
The Full Picture — What You Now Know
Your text
↓
Tokenizer → splits into tokens
↓
Embedding table → each token becomes
a list of numbers
↓
Positional encoding → adds position info
↓
Transformer layers (many of them)
→ Each layer runs Attention
(every token looks at every other token)
→ Then Feed Forward Network
→ Embeddings get richer and richer
↓
Final layer → probability distribution
over all vocabulary tokens
↓
Sample next token (controlled by temperature)
↓
Add to sequence → repeat until done
↓
Tokenizer converts numbers back to text
↓
Response appears on your screen
3-Line Summary
- The Transformer processes all tokens at the same time — not one by one — which makes it faster and better at understanding relationships across long text.
- It's made of many identical layers stacked on top of each other — early layers learn simple patterns, later layers learn complex reasoning — all using an Attention mechanism.
- More layers and more parameters means more capacity to understand language — which is why GPT-4 with ~1 trillion parameters is so much more capable than earlier models.
Module 2.3 — Complete ✅
Coming up — Module 2.4 — The Attention Mechanism
This is the actual secret sauce inside every Transformer. The word "attention" gets thrown around a lot — but almost nobody explains what it actually does in simple terms. We'll fix that completely. By the end you'll genuinely understand why that 2017 paper was called "Attention Is All You Need."
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