Understanding Transformer Jargon with Everyday Analogies: For Curious Minds of All Ages

Transformer models have taken AI world by storm. It has so many steps and jargons. Transformer jargons look like Alien language for all non-tech enthusiast. In this articles, I will try to breakdown complex terms with simple analogies so that even school grade students can understand this models easily.

Transformer model first appeared in 2017 in Attention is All You Need article which was published by Google. Transformer architecture is backbone of all of the current LLM(GPT, Llama etc.).

Think Transformer as Smart Student in Classroom. When class teacher is explaining a topic, each of the word(Token) is not important. Can you think what smart student do? Smart student does not pay attention to every word for understanding the concepts. They pay more attention to the important parts and connect them with what they already know and ignore unnecessary words.

That’s how transformer works. It reads data, then decides “कहाँ ध्यान देना है” using its attention mechanism. The Transformer focuses on the most meaningful parts of a sentence to understand or generate language.

Now comes to components of Transformer modes. Transformer models has two components - Encoder and Decoder. Models are Encoder only like BERT, Decoder only like GPT and Encoder-Decoder both like Text-To-Text Transfer Transformer.

Encoder - Think as Good Listener

“Most of the successful people I've known are the ones who do more listening than talking - Bernard Baruch“.

When teacher is explaining the concepts, smart students only listen. Exactly this is encoder in transformer.

Input Sentence is Teacher explaining about concepts. Encoder reads the entire sentence and decides "कहाँ ध्यान देना है" using the self-attention mechanism. It assigns different weights to each word depending on context. For Ex - Photosynthesis get full attention, but is, the get less attention.

So, now you can understand what exactly do encoder? Yes, you are right. Encoder turns the sentence into a meaningful internal representation, just like a student understanding the essence of a lecture.

Decoder - Think as Intelligent Speaker

“Good listener is good speaker too”.

Now, Teacher ask a question. Smart student (Decoder) uses what they understood from encoder’s output and respond very carefully with own word (token) step by step.

Decoder starts generating answer one word at a time. At each steps, looks back at what it has said so far (Masked Self-Attention) and refers to the encoder’s understanding (cross-attention). It keeps doing this until the full output.

So, now you can understand what exactly do decoder? Yes, you are right. Decoder is like the student who builds their answer gradually, checking both their understanding of the lecture and what they've already said.

Internal Deep Dive of Encoder

• 1) Tokenization - Think as breaking lecture into understandable bits. How exactly smart student (transformer) understand the concept? They need to break the sentence down into smaller, bite-sized pieces they can actually process. This step is tokenization.

  • Why do we need tokenization?

    Let’s suppose smart student does not understand English language. What can they do? They map English word with Hindi word and try to understand. Same way mathematical model do. Model does not understand English language, only understand Numbers. So, during tokenization process, token maps to number so that model can understand and learn better.

  • How do we map to numbers?

    The student has a dictionary of known words or pieces. Vocabulary size is how many unique tokens the model knows. If the vocab size is 50,000, it means the model can recognize 50,000 unique tokens.

  • "Photosynthesis" → ["Photo", "##synthesis"]

  • "Photo" → 2145, "##synthesis" → 5121

• 2) Vector Embedding - Now, smart student has understandable chunks after tokenization. Only word recognition is not important but the meaning of that word is also important. So, smart student form a mental picture of each word with meaningful though. So, Vector embedding turns each word/token into a rich, meaningful thought.

  Each token is mapped to a vector of numbers.

  • "sun" → [0.27, -0.81, 0.13, ..., 0.45]

  • "moon" → [0.23, -0.79, 0.17, ..., 0.49]

These vectors are learned during training and capture meaning, context, relationships with other words. The student doesn’t just see the word "photo" as label #2145—it sees a cloud of meaning around it.

• 3) Positional Embedding - Now, smart student has done the tokenization and embedding the word into meaningful vectors. But, they does not know about the order of words.

  For ex - “Cat sat on mat“ is not same as “Mat sat on cat“. Here, student must remember about the order of words. In models, this is positional encoding.

  • Token Embedding + Positional Embedding = Mental Notes

Transformers don’t process input sequentially (like RNNs), they look at the whole sentence at once, so they need some signal to know the order of the words.

• 4) Multiheaded Self Attention - Each attention head means a different perspective. Each head gives a different angle of understanding.

  For ex - Rahul is going to study transformer model. First head may be focus on “Who“, second head may be focus on “Where“, third head may be focus on “What“ etc.

  So, now you can understand what exactly do Multiheaded self attention? Yes, you are right. It captures different relationship, learn different patterns, focus on both local & global context.

• 5) Feedforward Neural Network - After giving attention to what is important and looked at different perceptive, deeper thinking is required for deeper understanding. Feedforward layer as the student’s inner processor, taking the attended info from each word and thinking. It’s often called position-wise feedforward, because it applies the same small neural net to each position separately think as solo mini-coaching session.

• 6) Residual Layer - When smart students write down notes, they don’t throw away the original sentence or completely rewrite it, they simply add their new understanding on top of their initial notes.

  • Final thought = Original thought + Deep insight (Think as Sticky notes)

This layer is important to preserve the original signal, avoid vanishing gradients and train faster & deeper.

• 7) Layer Normalization - Now the notes are a bit messy—some words are overemphasized, some under-explained, and the overall understanding is a bit imbalanced. So, Layer normalization is like student cleans up messy notes before going to next topic. In model, it ensures consistent scale of values, stable training and better gradient flow.

  Internal Deep Dive of Decoder

  Some of the concepts are same as encoder what i explained above. In this section, i will explain about decoder components.

• 1) Masked Self-Attention - Teacher has already explained the topic. Now, smart students is giving the test what teacher already explained in this class. During writing answer, student can only see what is already been written. They will write words step by step by seeing previous context. In transformer models, masked attention do similar work during decoding stage. It does not expose future words so that model can’t cheat during generating words and achieve truly autoregressive. In model, mask future attention weights (set to -infinity).

• 2) Cross Attention - Think as taking help from notes. Let assume student have permission to take reference from note during giving answer. So, when student is writing answer by generating their own answer but they can take help from notes. This is exactly cross attention.

  • Decoder - Student writing answer

  • Encoder - Sticky Notes

  • Cross Attention - Where in the sticky notes should I look to support my sentence?

The decoder generates one word at a time. At each step, it does Cross-Attention:

• Query = the current state of the decoder

• Key & Value = outputs from the encoder (i.e., input sentence)

• Attention scores help it attend to relevant parts of the input

• 3) SoftMax - Imagine a smart student is in a study circle with 5 classmates. They're trying to solve a tough question, and everyone gives a suggestion. But the student knows:

  • Not all suggestions are equally useful. I need to weigh them wisely.

So the student listens to each classmate, gives each response a mental score (how helpful it feels), like: Alice: 1.2 | Bob: 0.3 | Charlie: 2.1 | Dave: -0.4 | Emma: 1.0

Now, the student must decide:

• How much attention should I give to each classmate?

  • But these raw scores aren't percentages. So the student uses SoftMax to convert these raw scores into attention weights (i.e., probabilities that sum to 1).

    • Alice: 20% | Bob: 10% | Charlie: 45% | Dave: 5% | Emma: 20%

The student focuses mostly on Charlie, a bit on Alice and Emma, and almost ignores Dave.

Other Important Jargons

1) Temperature - Imagine our smart student is in a heated group debate. Everyone’s sharing ideas, and the student is scoring them in their mind. Temperature is like the student adjusting their mental strictness dial
Now the student has to decide how strongly to favor the best idea. That’s where temperature comes in.

• Low Temperature : Sharp focus, almost like a hard choice

• High Temperature : Softer focus, more exploratory

2) Top-p Sampling - Imagine a smart student is answering an open-ended question, like: Describe the water cycle.

They brainstorm multiple possible next phrases like: ["evaporates", "boils", "condenses", "sleeps", "rains", "teleports"]. Each word gets a probability based on how likely it is to come next (like from softmax).

The student doesn’t just pick the highest-probability word (like greedy search), nor blindly sample from all words (like pure randomness). “I’ll look at the top few most reasonable suggestions whose combined probability adds up to at least 90%, and pick from those only. That’s Top-p Sampling in action.

3) Knowledge cutoff - Imagine a super-smart student who has studied thousands of books, articles, and notes from a massive library. But, this student took a break from studying on a certain date, say September 2021.

Now they’re answering questions in an exam.

• Questions: Who is the winner of Champions Trophy-2025?

• Student Answer: Sorry, I stopped reading the news after 2021. I don’t know anything that happened after that.

That’s a knowledge cutoff.

Transformers work like smart students—focusing on important parts, ignoring noise, and building meaning step by step. From tokenization to attention and generation tricks like Top-p sampling, each layer adds intelligence. While powerful, their knowledge is limited by their last study date—their knowledge cutoff. Yet, they reason impressively well!