Fonyuy Gita

Posted on Aug 30

The Day Transformers Stared Back at Me😂

#gpt3 #programming #machinelearning #deeplearning

The Moment of Truth
The Journey Before Transformers
The Problem That Changed Everything
Enter the Transformer
Understanding Attention: The Heart of Magic
The Complete Transformer Architecture
Walking Through Our Example
Why This Changed Everything
From Transformers to ChatGPT
Resources and Further Learning

The Moment of Truth

When I first stood before my students to explain transformers, I thought I was simply going to teach. I had my Excalidraw, my diagrams, and my carefully chosen examples. But the look in their eyes told a different story — a mix of awe, confusion, and the quiet suspicion that I was pulling off some kind of sorcery. And honestly? I understood why. Transformers, with their strange talk of "attention" and "context," don't feel like code or math at first — they feel like magic. Yet, beneath that magic lies a beautifully human story about how machines learn to listen, focus, and create.

I'm writing this late at night because I believe understanding transformers is crucial for anyone working with modern AI. This explanation is designed for beginners—no heavy mathematics, just clear concepts that build understanding step by step.

We must know what transformers are, because everything we build today with LangChain, every ChatGPT conversation, every AI assistant—it all traces back to this one revolutionary idea.

The Journey Before Transformers

Neural Networks: The Foundation

Before we dive into the transformer magic, let me remind you of our journey. We started with the basics—neural networks. Think of a single neuron as a simple decision maker. It takes inputs, multiplies them by learned weights, adds them up, and decides whether to "fire" or not through an activation function.

This is where you saw the first "aha!" moment in class—when I showed you how stacking these neurons creates layers that can learn complex patterns. tho some people still shocked , like my boy here

Feed-Forward Networks: One Direction Only

Then we move to feed-forward networks. Information flows in one direction—input travels through hidden layers to reach the output. These networks excel at classification and simple prediction tasks, but they have a crucial limitation: they have no memory. Each input is processed independently, like looking at individual photographs without remembering what you saw in previous pictures.

The processing works in stages: first, the input data enters the network, then it passes through multiple hidden layers where each layer transforms the information further, and finally it reaches the output layer which gives us our result. This worked wonderfully for images and structured data, but it couldn't handle sequences where order matters.

This worked well for images and structured data, but what about sequences? What about text where the meaning of a word depends on the words that came before it?

RNNs: The Memory Keepers

Enter Recurrent Neural Networks (RNNs). These were our first attempt at giving networks memory. An RNN processes sequences one element at a time, maintaining a "hidden state" that carries information from previous steps.

RNNs were revolutionary! Suddenly we could process text, translate languages, and generate sequences. But they had problems:

Vanishing gradients: Information from early words would "fade away" in long sequences
Sequential processing: You had to process words one by one—no parallelization
Limited context: The hidden state became a bottleneck for long-range dependencies

This is where many people start to wonder: "If RNNs can handle sequences, why do we need something else?" The answer lies in understanding their fundamental limitations and how they affect real-world performance.

The Problem That Changed Everything

Imagine you're reading this sentence: "The cat that lived in the old house with the blue shutters and the garden full of roses was very friendly." By the time you get to "was very friendly," you need to remember that we're talking about "the cat"—not the house, shutters, or roses.

RNNs struggle with this. The information about "cat" gets diluted as it passes through all those intermediate words. We needed a way for the network to directly connect "was very friendly" with "cat," regardless of the distance between them.

We needed attention.

Enter the Transformer

In 2017, a Google research team published a paper titled "Attention Is All You Need." The title itself was revolutionary—it suggested we could throw away the sequential processing of RNNs entirely and rely purely on attention mechanisms.

The transformer architecture introduced a radical idea: instead of processing sequences word by word, what if every word could directly "attend" to every other word simultaneously?

let's simplify our architecture from what, is there on the left hand side to the right hand side

Let's work through a concrete example to understand this magic.

Understanding Attention: The Heart of Magic

Let's use this simple sentence as our example throughout: "The cat sat on the mat"

What is Attention?

Think of attention like a spotlight in a dark theater. When an actor is speaking, the spotlight doesn't just illuminate them—it also subtly highlights the props, other actors, and stage elements that are relevant to understanding the current moment.

In transformer terms, when we're processing the word "sat," attention helps us figure out:

WHO is sitting? (The cat)
WHERE are they sitting? (on the mat)
WHAT is the relationship between all these words?

The Three Components: Query, Key, Value

Think of these three components like a sophisticated library system. When you walk into a library looking for information about a specific topic, you need three things to work together:

Query (Q) represents what you're looking for. It's like walking up to the librarian and saying "I need information about medieval history." This query contains the essence of what you want to find.

Key (K) represents what each book or resource can tell you about. Every book has a catalog card that describes its contents. Some books might have keys that say "I contain information about medieval history," while others might say "I contain information about modern science."

Value (V) represents the actual content or information contained in each resource. This is the detailed information you'll actually use once you find the right match.

Let's see this in action with our sentence "The cat sat on the mat":

When we process the word "sat," its query essentially asks: "I am an action word, looking for who performs me and where I take place." The word "cat" has a key that responds: "I am a subject, I can tell you WHO does actions." The word "mat" has a key that says: "I am an object, I can tell you WHERE things happen."

The attention mechanism calculates how well each query matches with each key. When "sat" queries for its actor and location, it finds strong matches with both "cat" and "mat," but weak matches with words like "the" which don't provide much relevant information for understanding the action.

Computing Attention Scores

The beautiful elegance of attention lies in its mathematical simplicity. Here's how the process works conceptually:

First, we calculate similarity scores between each query and every key in the sentence. This tells us how relevant each word is to every other word. Think of it like measuring the strength of connection between each pair of words in the sentence.

Next, these raw similarity scores get converted into probabilities using a process called softmax. This ensures that all the attention weights for each word add up to exactly 1.0, creating a probability distribution. It's like saying "I have 100% of my attention to distribute, how much goes to each word?"

Finally, we use these probability weights to create a weighted combination of all the values. This means each word gets updated with information from other words, where the amount of information depends on the attention weights calculated in the previous steps.

When we process "sat," it might attend to different words with different intensities: "cat" receives 60% attention (high relevance - who is sitting?), "mat" receives 30% attention (medium relevance - where is the sitting?), and each occurrence of "the" receives only 5% attention (low relevance - not crucial for understanding the action).

This is the magical moment that captures people's imagination: every word can simultaneously consider its relationship with every other word in the sentence, creating a rich web of contextual understanding.

The Complete Transformer Architecture

Now let's build the complete transformer step by step, using our example sentence.

Input Processing and Embeddings

The first step in understanding how transformers work is grasping how they convert words into numbers that computers can process. This process is called creating embeddings, and it's fundamental to everything that follows.

Imagine if we could represent every word as a point in a multi-dimensional space, where words with similar meanings cluster together. The word "cat" might be close to "dog" and "pet," while "sat" would be near "stood" and "positioned." This spatial representation captures semantic relationships between words.

Our sentence ["The", "cat", "sat", "on", "the", "mat"] gets transformed into a series of numerical vectors. Each word becomes a list of several hundred numbers that represent its meaning, grammatical properties, and relationships to other words. These vectors are learned during training, so words that appear in similar contexts end up with similar representations.

The beauty of embeddings is that mathematical operations on these vectors correspond to meaningful operations on word meanings. The transformer uses these numerical representations to perform all its subsequent processing steps.

Positional Encoding

This is where transformers solve a fascinating puzzle. Unlike RNNs which process words one at a time and naturally know which word comes first, second, and so on, transformers look at all words simultaneously. This parallel processing is what makes them so fast and powerful, but it creates an interesting problem: how does the model know the order of words?

The solution is positional encoding, a clever mathematical technique that adds position information directly to each word's embedding. Think of it like adding a unique "address" or "timestamp" to each word that tells the model exactly where it sits in the sentence.

These positional encodings use sine and cosine wave patterns at different frequencies. Why sine and cosine waves? Because they create unique patterns for each position that have useful mathematical properties. Words that are close together in the sentence get similar positional patterns, while words far apart get very different patterns.

The transformer adds these positional patterns to the word embeddings, so now each word representation contains both its semantic meaning and its exact position in the sentence. This combined information flows through all the subsequent processing steps, ensuring that word order is never lost even though all words are processed simultaneously.

This step transforms our simple word embeddings into position-aware representations that know both what each word means and where it appears in the sequence.

The Encoder Stack

The encoder processes our input sentence to create rich, context-aware representations. Each encoder layer has two main components:

Multi-Head Attention

Instead of having just one attention mechanism, transformers use multiple attention "heads" that work in parallel, each focusing on different aspects of the relationships between words. Think of this like having multiple experts examining the same sentence, where each expert specializes in noticing different types of connections.

One attention head might specialize in subject-verb relationships, becoming highly attuned to connections like "cat" relating to "sat." Another head might focus on prepositional relationships, expertly identifying how "sat" connects through "on" to "mat." A third head might concentrate on determiner relationships, understanding how articles like "the" modify their associated nouns.

The power of multi-head attention lies in this parallel specialization. While one head is figuring out who performs which action, another head simultaneously works out spatial relationships, and yet another identifies grammatical structures. Each head develops its own understanding of the sentence, and these different perspectives are then combined to create a comprehensive understanding.

The process works by taking the input embeddings and creating separate query, key, and value representations for each attention head. Each head performs its attention calculations independently, focusing on the patterns it has learned to recognize. After all heads complete their processing, their outputs are combined together and transformed to create the final representation that captures insights from all the different specialized perspectives.

Feed-Forward Network

After the multi-head attention mechanism enriches each word with contextual understanding, the information passes through what's called a feed-forward network. Think of this as a refinement process where each word's enriched representation gets individually processed and enhanced.

The feed-forward network works like a specialized filter for each word position. It takes the attention-enhanced representation and applies two successive transformations. The first transformation expands the representation into a higher-dimensional space, allowing the network to explore more complex patterns and relationships. The second transformation then compresses this expanded representation back down to the original size, but now with refined and processed information.

This process happens independently for each word position, but using the same learned transformation patterns. It's like having the same expert editor review each word individually, applying consistent standards and improvements while respecting the unique context each word has acquired from the attention process.

The feed-forward network serves as a crucial processing step that allows the transformer to make complex, non-linear transformations to the information gathered by attention, ultimately creating richer and more nuanced representations of each word in its context.

Residual Connections and Layer Normalization

Two crucial architectural elements make the transformer both trainable and stable: residual connections and layer normalization. These might sound technical, but they solve very practical problems that arise when building deep neural networks.

Residual connections, also called skip connections, create shortcuts that allow information to flow directly from the input of a layer to its output, bypassing the processing steps in between. Think of this like having both a winding mountain road and a tunnel that goes straight through the mountain. The information can take both paths and combine at the destination.

Why is this important? As neural networks get deeper with more layers, they can suffer from a problem where the learning signal becomes too weak by the time it reaches the early layers. Residual connections solve this by ensuring that every layer receives direct access to the original information, making the network much easier to train effectively.

Layer normalization acts like a quality control mechanism that keeps the numerical values in each layer within reasonable ranges. Without this, the values could grow very large or very small as they pass through multiple layers, making the network unstable and difficult to train. Layer normalization standardizes these values, ensuring consistent and stable processing throughout the network.

Together, these mechanisms create a robust architecture where information flows smoothly through many layers, each layer can receive clear learning signals, and the entire system remains stable during training. This combination is what makes it possible to train transformers with billions of parameters successfully.

After passing through all encoder layers, each word has a rich representation that captures its meaning in context with all other words.

The Decoder Stack (For Generation Tasks)

The decoder represents the part of the transformer that generates new text, and understanding it requires grasping a fundamental difference from the encoder. While the encoder can look at all words in the input sentence simultaneously, the decoder must work under a crucial constraint: when generating text word by word, it can only look at words it has already generated, never at future words it hasn't created yet.

Think of the decoder like a person writing a story who can look back at everything they've written so far, but obviously cannot peek ahead at what they're going to write next. This constraint exists because during text generation, those future words simply don't exist yet.

Let's imagine we're translating our sentence "The cat sat on the mat" into French: "Le chat était assis sur le tapis." When the decoder is generating the word "était" (was), it can look back at "Le chat" (the cat) that it already generated, but it cannot look ahead to "assis sur le tapis" (sitting on the mat) because those words haven't been generated yet.

This creates what we call "masked attention." The decoder uses a special form of attention that literally masks or hides future positions, preventing the model from accidentally using information from words that don't exist yet in the generation process.

The decoder also has a second attention mechanism called encoder-decoder attention. This allows the decoder to look at the original input sentence (in our example, the English sentence) while generating each word of the output sentence (the French translation). So when generating "était," the decoder can attend to relevant words in the English input like "sat" to understand what French word should come next.

This dual attention system makes the decoder incredibly powerful: it maintains coherence with what it has already generated through masked self-attention, while staying faithful to the original meaning through encoder-decoder attention.

Walking Through Our Example

Let's trace our sentence "The cat sat on the mat" through the complete transformer:

Step 1: Tokenization and Embedding

Input: "The cat sat on the mat"
Tokens: [1, 156, 234, 67, 1, 891]  # Token IDs
Embeddings: 6 x 512 dimensional vectors

Step 2: Add Positional Encoding

Each word now knows its position in the sentence.

Step 3: Encoder Processing

Layer 1 Attention Scores (simplified):

       The  cat  sat  on   the  mat
The    0.1  0.1  0.1  0.1  0.5  0.1   # "The" attends mostly to other "the"
cat    0.1  0.2  0.6  0.1  0.0  0.0   # "cat" attends strongly to "sat"
sat    0.0  0.4  0.2  0.3  0.0  0.1   # "sat" attends to "cat", "on"
on     0.0  0.1  0.3  0.1  0.1  0.4   # "on" connects "sat" and "mat"
the    0.1  0.0  0.0  0.1  0.1  0.7   # Second "the" attends to "mat"
mat    0.0  0.1  0.2  0.3  0.1  0.3   # "mat" understands it's the target

Step 4: Multiple Layers Build Understanding

As we go through encoder layers 2, 3, 4, 5, 6, the representations become richer:

Layer 1: Basic word relationships
Layer 2: Grammatical structures (subject-verb-object)
Layer 3: Semantic relationships (who does what to whom)
Layer 4-6: Abstract concepts and complex dependencies

Step 5: Final Output

Each word now has a 512-dimensional vector that captures:

Its meaning
Its grammatical role
Its relationships with other words
Its position and context

This is what makes transformers so powerful—every word has been enriched with understanding of the entire sentence context.

Why This Changed Everything

Parallel Processing

Unlike RNNs that process words sequentially, transformers process all words simultaneously. This means:

Much faster training on modern GPUs
Better utilization of parallel computing
Ability to train on massive datasets

Long-Range Dependencies

Remember our RNN problem with long sentences? Transformers solve this completely. The attention mechanism can connect any two words directly, regardless of distance.

Scalability

The transformer architecture scales beautifully. Want to handle longer sequences? Just add more attention heads. Want deeper understanding? Add more layers. Want to process more data? The parallel nature makes it efficient.

Transfer Learning

Pre-trained transformers can be fine-tuned for specific tasks, leading to the explosion of practical AI applications.

From Transformers to ChatGPT

The transformer architecture led to a series of breakthroughs:

GPT Series (Decoder-Only)

GPT-1 (2018): Showed that transformers could generate coherent text
GPT-2 (2019): So good at generating text, OpenAI initially didn't release it
GPT-3 (2020): 175 billion parameters, breakthrough in few-shot learning
ChatGPT (2022): GPT-3.5 with human feedback training
GPT-4 (2023): Multimodal capabilities, even better reasoning

BERT (Encoder-Only)

Google's BERT revolutionized text understanding tasks like search and question-answering.

T5 (Encoder-Decoder)

"Text-to-Text Transfer Transformer" treated every NLP task as text generation.

Modern Applications

Every AI tool you use today likely uses transformers:

LangChain applications
Code assistants like GitHub Copilot
Translation services
Content generation
Chatbots and virtual assistants

The Magic Revealed

When my students stared at me with that "what is he talking about" look, they were witnessing the moment where simple mathematical operations combine to create something that feels like understanding.

The transformer doesn't just process text—it creates a web of relationships, a map of meaning, a context-aware understanding that captures the essence of language itself. Every attention head is like a different lens for looking at text, every layer builds deeper understanding, and every connection helps the model "think" about language the way humans do.

This is why we can build AI applications with LangChain that seem to truly understand our intentions. This is why ChatGPT can hold coherent conversations. This is why code assistants can understand context across entire files.

The magic isn't in the math—it's in the emergence of understanding from simple, repeated patterns of attention and transformation.

Key Takeaways for GenAI Engineers

As you build AI applications with tools like LangChain, remember:

Context is King: Transformers excel because they understand context. Design your prompts and chains to provide rich context.
Attention Patterns Matter: Understanding how attention works helps you craft better prompts and understand model limitations.
Layer by Layer: Complex understanding emerges from simple operations repeated many times. Your applications benefit from this layered understanding.
Parallelization: The efficiency of transformers makes real-time AI applications possible.
Transfer Learning: Pre-trained transformers can be adapted to your specific use cases through fine-tuning or few-shot learning.

Resources and Further Learning

Essential Papers

Attention Is All You Need - The original transformer paper
BERT: Pre-training of Deep Bidirectional Transformers
Language Models are Few-Shot Learners - GPT-3 paper

Interactive Learning

The Illustrated Transformer by Jay Alammar
Transformers from Scratch by Peter Bloem
The Annotated Transformer - Line-by-line implementation

Practical Implementation

Video Explanations

Books

"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
"Natural Language Processing with Transformers" by Lewis Tunstall, Leandro von Werra, and Thomas Wolf

Final Thoughts

The day transformers stared back at me was the day I realized we were witnessing something truly special. Not just a new architecture, not just better performance metrics, but a fundamental shift in how machines can understand and generate human language.

Every time you use ChatGPT, every time you build an application with LangChain, every time an AI assistant understands your intent—you're witnessing the magic of attention, the power of context, and the beauty of transformers.

The confused looks in my students' eyes have now turned to understanding, and I hope this journey from basic neural networks to the transformer revolution helps you appreciate the elegant simplicity behind what seems like magic.

Remember: at its core, a transformer is just a very sophisticated way of asking, "Given all the words I've seen, what should come next?" But in that simple question lies the key to artificial intelligence that can understand, create, and communicate.

The future of AI is built on the foundation of transformers, and now you understand why.

NOTE

I initially thought this would take me just 2 days, but it ended up taking 3. Honestly, I can never fully grasp something in just a day, two days, or even a week—you really have to keep going over it again and again, like a neural net, before the concept clicks. I hope this gives you a sense of what transformers are. I also recommend checking out the videos in the resources section—they’ll be a great help.

Drop your thoughts in the comments, and don’t forget to give it a like!

Top comments (16)

Thomas TS • Sep 3

I know a bit about non-linear systems. Usually represented by differential equations. They are retro feed systems where the output is redirected to the input. Many of those create what were named Fractals. Fractals can be understood as 'attractors', like the Lorentz's attractor for atmospheric numeric models. I was wondering if in an N-dimensional space of an embedding, meaning would produce fractals...

Fonyuy Gita • Sep 4

That's a fascinating connection! 🤔 Think of it like this: if words are stars in meaning-space, then maybe related concepts naturally cluster into beautiful spiral galaxies (fractals). Each time we feed context back into the transformer, we're like astronomers discovering new constellations of understanding. The universe of language might indeed have its own strange attractors

Mike Angel • Sep 4

Wow 😳😲 Brilliant

iws technical • Sep 4

waoh!

Thomas TS • Sep 4

In fact, I had a very satisfactory conversation with GPT about this subject. If you are interested, let me know and we find a way to communicate.

fonyuy jude • Sep 5

Let's go

Thomas TS • Sep 5

I tried to find some way to contact you at Vercel, Github, Likedin, etc... but none of those places offer any lead about that. As I do not use Twitter, here is the only place to talk. Maybe we could talk at Discord: ttsoares#2710.