It processes relationships between tokens before producing an output.
Core idea
Architecture first. Mechanism inside.
A Transformer is a neural-network architecture that processes relationships between tokens.
Its attention mechanism lets each token weigh information from other relevant tokens.
TOKENSThe bank approved it
→
ATTENTIONcompare relationships
→
LAYER STACKrefine representations
→
OUTPUTprediction / representation
Architecture map
Transformer at a Glance
INPUT TOKENS
↓
TOKEN REPRESENTATIONS
↓
ATTENTION
↓
FEED-FORWARD PROCESSING
↓
REPEAT THROUGH LAYERS
↓
OUTPUT REPRESENTATION / SCORES
A useful analogy
Think of it like a reading group
Thebankapprovedit
Each word can look at the other words and decide which ones are most useful for understanding the current context.
Reading group → Transformer layer
Words → Tokens
Looking at other words → Attention
Repeated discussion → Layer stack
This analogy is for understanding only. A Transformer uses mathematical operations, not human discussion.
The processing story
How it works
STEP 01
Tokens enter the model
Text becomes token representations.
STEP 02
Attention compares relationships
Each position can weigh information from other relevant positions.
STEP 03
Representations are updated
Attention results are combined with additional neural-network processing.
STEP 04
Layers repeat the process
Stacked layers progressively transform the representation.
STEP 05
Output scores are produced
The final representation supports prediction or another model task.
A simplified illustration
Why attention matters
“The bank approved the loan because it had enough information.”
itcan use context from:bankloaninformation
Attention assigns different weights to relationships between token representations. This is a simplified illustration, not a visualization of a specific model’s real attention weights.
Supporting examples
Where Transformers are used
01
Language understanding
Example: Pronouns and context
02
Translation
Input: English Output: another language
03
Classification
Input: support ticket Output: category
Concept boundaries
What it is NOT
TRANSFORMER ≠ LLM
Architecture ≠ language model
An LLM is often built with Transformer architecture.
TRANSFORMER ≠ ATTENTION
Full architecture ≠ one mechanism
Attention is one important mechanism inside it.
TRANSFORMER ≠ CHATGPT
Architecture ≠ product
ChatGPT uses models plus other systems.
Connections
Related concepts
Neural Networksbroader family
Attentionkey mechanism
Layersrepeated building blocks
Tokenscommon sequence units
LLMoften built using Transformers
Context Windowavailable context amount
KV Cacheinference optimization
Remember this
A Transformer uses attention and stacked neural-network layers to process relationships between tokens.
Independent explainer · Azure Neural voice
Watch the visual explanation
See how tokens, attention, layers, and output scores fit together.