TRANSFORMER BASICS

Pranshu Tiwari — Sun, 04 Jan 2026 11:54:27 +0000

Analogy Setup:

Imagine the Transformer is a Bollywood director making a blockbuster film.
Input sentence = script idea
Encoder = team understanding the script
Decoder = actors performing dialogues
Self-attention = each actor checking others’ lines to stay in context
Cross-attention = actors looking at director’s guidance
Final output = movie dialogue or scene
1️⃣ Input Tokenization
Script broken into scenes or lines
Example:
“I love AI” → ["I", "love", "AI"]
Analogy: Director splits script into dialogues for actors.
2️⃣ Input Embedding
Each word → vector of numbers
Captures meaning
Analogy: Actor memorizes character traits and emotions for each dialogue line.
3️⃣ Positional Encoding
Adds word order info
Analogy: Director marks scene order: First scene, second scene… to maintain storyline.
4️⃣ Self-Attention
Words check important words around them
Analogy: Actors listen to other actors’ dialogues to maintain chemistry & context.
“Bank” listens to “deposit” to know it’s financial, not river.
5️⃣ Multi-Head Attention
Multiple “attention heads” look at different aspects
Analogy: Multiple camera angles filming: close-up, wide-shot, overhead → complete scene understanding.
6️⃣ Add & Normalize (Residuals)
Original info + attention output → stabilized
Analogy: Actors keep original character traits while adding director’s inputs.
7️⃣ Feed Forward Network
Each word refined individually
Analogy: Actors rehearse solo to perfect expressions before final scene.
8️⃣ Decoder Input (Shifted Right)
Decoder sees previous words only
Analogy: Actors deliver next line based on previous dialogue, not future scenes.
9️⃣ Masked Self-Attention
Future words hidden
Analogy: Actor doesn’t know upcoming twist in the movie.
10️⃣ Encoder–Decoder Attention
Decoder focuses on relevant encoder output
Analogy: Actor looks at director’s notes to align with story context.
11️⃣ Decoder FFN + Add & Normalize
Refines token and stabilizes
Analogy: Actor practices solo again, keeping director’s guidance in mind.
12️⃣ Linear + Softmax

Converts decoder output → word probabilities → final word chosen
Analogy: Actor picks best dialogue delivery for scene.

🎬 Complete Flow

Script → Tokenization

Actors understand script → Embedding + Positional Info

Actors rehearse → Self-Attention + Multi-Head Attention

Director guides them → Cross-Attention

Final performance → Linear + Softmax → Scene delivered

Repeat for next scene → Complete movie

DEV Community: Pranshu Tiwari

TRANSFORMER BASICS