Understanding Transformer Architecture: The Brains Behind Modern AI

Transformers have fundamentally reshaped the AI landscape — powering models like ChatGPT and driving major innovations across Google Search, recommendation engines, and enterprise analytics. From smarter user interfaces to advanced automation and real-time insight generation, transformer-based models such as GPT, BERT, and T5 are enabling businesses to streamline workflows, personalize customer experiences, uncover valuable insights, and accelerate product development.

But what makes transformers so effective, and how do they actually work?

In this blog post, we’ll break down the architecture that powers modern AI. You’ll learn why transformers were invented, the differences between encoder-only, decoder-only, and full transformer models, and when each is best suited — whether you’re a data scientist or machine learning engineer building applications, a product manager making roadmap decisions, or a business leader evaluating AI’s strategic value. We’ll also explore how the inner components work, with diagrams and practical examples to ground the theory in real-world use.

🚀 Motivation

Before transformers, models like RNNs and LSTMs were used for sequential data. These models process tokens one at a time, which limits parallelism and struggles with long-range dependencies.

Transformers changed the game by using self-attention, which allows each token to directly consider all other tokens in the sequence simultaneously — capturing relationships between words, no matter how far apart they are.

This makes transformers faster to train, more scalable, and dramatically more powerful.

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🧱 Transformer Architectures: Full, Encoder-Only, Decoder-Only

There are three main variants of the transformer architecture, each optimized for different types of tasks:

Architecture	Example Models	Best For
Full Transformer	T5, BART, MarianMT	Translation, summarization, multimodal
Encoder-Only	BERT, RoBERTa	Classification, sentence similarity, QA
Decoder-Only	GPT-2/3/4, LLaMA, PaLM, Claude	Text generation, chat, code completion

Why Each Fits Its Application

• Encoder-only models create contextual representations of text — ideal for understanding tasks.
• Decoder-only models generate text token by token, making them ideal for chat, storytelling, coding, and completion.
• Encoder–decoder models separate input and output — perfect for sequence-to-sequence tasks like translation.

📊 Architecture Diagrams

Full Transformer (Encoder–Decoder)

Input Tokens (e.g. English)
   ↓
[Encoder Stack]
   ↓
Context Representations
   ↓
[Decoder Stack with Cross-Attention]
   ↓
Output Tokens (e.g. French)

Encoder-Only Transformer

Input Tokens
   ↓
[Encoder Stack]
   ↓
Contextual Embeddings → used for classification or sentence-level tasks

Decoder-Only Transformer

Prompt/Input Tokens
   ↓
[Decoder Stack with Masked Self-Attention]
   ↓
Autoregressive Output → one token at a time

🧱 Detailed Encoder Stack (Used in BERT, T5)

Each encoder layer in the stack follows this sequence:

1. Token Embeddings + Positional Encoding

What it does:

Converts each input token into a vector and adds a positional signal.

• Each word (like “The”, “cat”, “sleeps”) is converted into a vector of numbers that captures the meaning of the word.
• Adds information about word order (like “first”, “second”, etc.) to each word vector.

Why it’s needed:

• The model can’t work directly with text. It needs a numerical understanding of words. These embeddings capture similarities, like “cat” and “dog” has a larger similarity than “cat” and “sleep”.
• Transformers don’t know order by default. So “the cat sleeps” and “sleeps cat the” would look the same without this. Positional encoding tells the model who came before and after.

How it works:

Each input token (like “The”, “cat”, “sleeps”) is mapped to a vector using a learned token embedding matrix. These vectors represent the meaning of each token in a high-dimensional space. Let’s break it down:

Token Embeddings:

Given:

• Vocabulary size: \(S_v\)
• Embedding dimension: \(d\)
• Embedding matrix: \(E_{\text{token}} \in \mathbb{R}^{S_v \times d}\)

If your sentence is ["The", "cat", "sleeps"], and these map to token IDs [12, 45, 230], then:

\[X = \begin{bmatrix} E_{\text{token}}[12] \\ E_{\text{token}}[45] \\ E_{\text{token}}[230] \end{bmatrix} \in \mathbb{R}^{3 \times d}\]

These vectors are learned during training via backpropagation.

Positional Encoding:

We add a vector to each token embedding that tells the model its position in the sequence. There are two variants:

• Sinusoidal PE (fixed):

\[\text{PE}[p, 2i] = \sin\left(\frac{p}{10000^{2i/d}}\right), \quad \text{PE}[p, 2i+1] = \cos\left(\frac{p}{10000^{2i/d}}\right)\]

• Learned PE: \(E_{\text{pos}} \in \mathbb{R}^{L \times d}\), where \(L\) is max sequence length.

Final Input:

For position \(p\) with its implied token ID \(t_p\):

\[X_p = E_{\text{token}}[t_p] + \text{PE}[p]\]

This combined vector \(X_p\) which encodes both what the word is and where it is becomes the input to the first transformer layer.

2. Multi-Head Self-Attention

What it does:

Each word looks at all the other words in the sentence and decides how much attention to pay to each of them.

Why it’s needed:

This helps the model understand context. Multi-head means this is done in multiple ways at once. One head might look at subject-verb, another at adjectives, etc. For example, if the word is “sleeps”, attention helps it realize that “cat” is the subject performing the action.

How it works:

1. Linear projections for Q, K, V

Each input token vector \(x_i \in \mathbb{R}^{1 \times d}\) from the input matrix \(X \in \mathbb{R}^{n \times d}\) is transformed into:

• Query vector: \(Q_i = x_i W^Q \in \mathbb{R}^{1 \times d_k}\)
• Key vector: \(K_i = x_i W^K \in \mathbb{R}^{1 \times d_k}\)
• Value vector: \(V_i = x_i W^V \in \mathbb{R}^{1 \times d_k}\)

where \(W^Q, W^K, W^V \in \mathbb{R}^{d \times d_k}\) are learned weight matrices, and \(d_k\) is typically \(d / h\), with \(h\) being the number of heads.

Stacking across all tokens:

\[Q = X W^Q \in \mathbb{R}^{n \times d_k}\] \[K = X W^K \in \mathbb{R}^{n \times d_k}\] \[V = X W^V \in \mathbb{R}^{n \times d_k}\]

2. Compute attention scores

For each query-key pair, compute a score:

\[\text{score}_{ij} = \frac{Q_i K_j^T}{\sqrt{d_k}}\]

This measures how much token \(i\) should attend to token \(j\).

3. Apply softmax

Convert scores to attention weights:

\[\alpha_{ij} = \text{softmax}_j\left(\text{score}_{ij}\right)\]

Each \(\alpha_{ij} \in [0,1]\), and \(\sum_j \alpha_{ij} = 1\).

Intuition:

• The softmax function turns raw scores into probabilities.
• It highlights the tokens with higher relevance by assigning them larger weights, while downplaying less relevant tokens.
• In other words, it helps the model focus more on important words — like attending to “cat” when decoding “sleeps”.

4. Weighted sum of values

Use the attention weights to combine the values:

\[\text{output}_i = \sum_j \alpha_{ij} V_j\]

This is the context-aware representation for token \(i\).

So, the full attention operation is:

\[\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right) V\]

This function maps the queries to a weighted sum of values, using scores derived from the keys.

5. Do this for multiple heads

Repeat the attention process above \(h\) times, each with its own set of learned \(W^Q, W^K, W^V\) matrices:

\[\text{head}_i = \text{Attention}(Q^{(i)}, K^{(i)}, V^{(i)}) \in \mathbb{R}^{n \times d_k}, \quad i = 1, \dots, h\]

6. Concatenate and project

Concatenate all the heads along the feature dimension and apply a final linear projection:

\[\text{MultiHead}(X) = \text{Concat}(\text{head}_1, ..., \text{head}_h) W^O\]

where \(W^O \in \mathbb{R}^{(h d_k) \times d}\) is also learned.

\(W^O\) determines how to combine the different “perspectives” from all attention heads into a single, unified vector that can be used by the next layer. It decides how much weight to give to each head’s output, essentially blending them into a coherent representation for each token.

Intuition:

• Queries ask questions: “Who’s relevant to me?”
• Keys answer: “Here’s what I have to offer.”
• Values carry content.
• Multi-head = many ways to look at the same sentence — one head might focus on syntax, another on long-distance relationships.

Each output vector is a blend of others — how much it blends depends on the attention scores. That’s how the model learns context.

3. Add & LayerNorm (Residual Block 1)

What it does:

Adds the attention output back to the original input (residual connection), then applies layer normalization.

Why it’s needed:

• Helps prevent vanishing gradients and training instability
• Preserves the original token information and combines it with what attention learned
• Keeps the scale of values consistent across layers

How it works:

\[Z = \text{LayerNorm}(X + \text{MultiHead}(X))\]

This applies Layer Normalization to the sum of the input matrix \(X\) and the multi-head attention output, producing output matrix \(Z \in \mathbb{R}^{n \times d}\).

LayerNorm is applied per token, i.e., on each row \(z_i \in \mathbb{R}^{1 \times d}\) of \(Z\). It normalizes the feature vector by adjusting its mean and variance:

\[\mu = \frac{1}{d} \sum_{i=1}^{d} z_i, \quad \sigma^2 = \frac{1}{d} \sum_{i=1}^{d} (z_i - \mu)^2\]

Then:

\[\text{LayerNorm}(z) = \gamma \cdot \frac{z - \mu}{\sqrt{\sigma^2 + \epsilon}} + \beta\]

where:

• \(z\) is one token vector from \(Z\)
• \(\gamma\) and \(\beta\) are learnable gain and bias vectors (\(\in \mathbb{R}^d\))
• \(\epsilon\) is a small constant for numerical stability

4. Feedforward Neural Network (FFN)

What it does:

Each token’s vector (after attention) is passed through a small neural network — the same one for every position — to refine its representation.

Each token goes through this same transformation separately — it’s position-wise, not sequence-wide.

Why it’s needed:

• Adds nonlinearity to the model
• Lets the model reprocess each token’s context-aware vector independently
• Helps the model abstract and compress information

For example, if the word “sleeps” attends to “cat” in attention, the FFN helps turn that into a refined idea like “subject performs action.

How it works:

It consists of two fully connected layers with an activation in between:

\[\text{FFN}(x_i) = \text{GELU}(x_i W_1 + b_1) W_2 + b_2 \in \mathbb{R}^{1 \times d}\]

• \(W_1 \in \mathbb{R}^{d \times d_{ff}}\), \(W_2 \in \mathbb{R}^{d_{ff} \times d}\)
• \(d_{ff}\) is usually 4× the size of \(d\) (embedding dimension)
• Activation is typically ReLU or GELU (more commonly used in transformers)

This is applied independently to each token vector in the sequence, with shared weights across positions.

Intuition:

• Think of this as a mini brain that processes each token in isolation.
• While attention lets tokens talk to each other, FFN helps each token reflect and reshape what it just learned.

5. Add & LayerNorm (Residual Block 2)

• Adds FFN output to input of FFN and normalizes again

This block is repeated \(N\) times to build deeper semantic understanding.

🧱 Detailed Decoder Stack (Used in GPT, T5, BART)

Each decoder layer includes all of the above plus masking and cross-attention:

1. Token Embeddings + Positional Encoding (in Decoder)

What it does & Why it’s needed:

Same as the encoder: tokens are converted into vectors and combined with positional encodings to retain order.

How it works (differences from encoder):

• Token embeddings and positional encodings are constructed the same way as in the encoder
• The key difference is that the decoder uses shifted inputs:

Target sequence:         ["The", "cat", "sleeps"]
Shifted decoder input:   ["<BOS>", "The", "cat"]

<BOS> stands for Beginning of Sequence. It’s a special token inserted at the start of the decoder input to indicate the start of generation.

• It has its own embedding like any other token.
• It helps the model know when and how to begin generating output.
• During inference, generation typically begins from alone.

Why the shift?

The decoder generates tokens one by one, using only the tokens that came before. During training, we shift the input so that:

• At each position \(p\), the model sees tokens \(< t_0, t_1, ..., t_{p-1} >\)
• And it is trained to predict \(t_p\)

This teaches the model to learn autoregressive generation — i.e., predict the next word based only on previously generated ones.

2. Masked Multi-Head Self-Attention (in Decoder)

What it does & Why it’s needed:

In the decoder, masked self-attention ensures that each token can only attend to earlier tokens — not to future ones.

• This is essential for autoregressive generation, where the model generates text one token at a time.
• Without masking, the model could “cheat” by peeking at the token it’s supposed to predict.
• Masking enforces strict causality in generation.

How it works (differences from encoder):

• In the encoder, attention is fully visible — each token can attend to all others.
• In the decoder, we apply a causal mask that blocks attention to future positions.

Steps:

1. Compute Q, K, V projections just like in the encoder:

   \[Q = XW^Q, \quad K = XW^K, \quad V = XW^V\]

2. Compute raw attention scores:

   \[\text{scores} = \frac{QK^T}{\sqrt{d_k}}\]

3. Apply causal mask: Set all positions \((i,j)\) where \(j > i\) to \(-\infty\):

   \[\text{scores}_{ij} = -\infty \text{ if } j > i\]

4. Apply softmax:

   \[\alpha_{ij} = \text{softmax}_j(\text{scores}_{ij})\]

5. Compute output:

   \[\text{output}_i = \sum_j \alpha_{ij} V_j\]

Each token only “looks left” — at the tokens that came before.

3. Add & LayerNorm (Residual Block 1)

• As in encoder

4. Cross-Attention (Encoder-Decoder only)

What it does

Cross-attention allows the decoder to look at the encoder’s output — i.e., the representation of the input sequence.

• Each token in the decoder can attend to all positions of the input.
• This provides context from the input sequence that guides the decoder’s output.

Why it’s needed:

• Without cross-attention, the decoder would only generate based on its own past outputs.
• In tasks like translation, the decoder needs to condition its next token prediction on the entire input sentence.
• Cross-attention lets the model align output tokens with relevant input tokens.

How it works:

• Similar to regular attention, but instead of \(Q\), \(K\), \(V\) all coming from the decoder, only \(Q\) comes from the decoder, and \(K\) and \(V\) come from the encoder.

Steps:

1. Use the decoder’s hidden states to compute queries:

   \[Q = X_{\text{decoder}} W^Q\]

2. Use the encoder’s output (fixed after encoding) to compute keys and values:

   \[K = X_{\text{encoder}} W^K, \quad V = X_{\text{encoder}} W^V\]

3. Compute attention scores:

   \[\text{scores} = \frac{QK^T}{\sqrt{d_k}}\]

4. Apply softmax to get weights:

   \[\alpha_{ij} = \text{softmax}_j(\text{scores}_{ij})\]

5. Compute weighted sum of values:

   \[\text{output}_i = \sum_j \alpha_{ij} V_j\]

This output is then passed forward in the decoder layer.

Intuition:

• Think of encoder-decoder attention as a bridge.
• The decoder is generating text and asks: “What did the input say that’s relevant right now?”
• Cross-attention provides that answer, using the encoder’s understanding of the input.

This mechanism is what allows sequence-to-sequence models to perform tasks like summarization, translation, and more.

5. Add & LayerNorm (Residual Block 2)

• Same structure as above

6. Feedforward Neural Network (FFN)

• Same as encoder

7. Add & LayerNorm (Residual Block 3)

• Final residual normalization

Decoder layers are also repeated \(N\) times for generation depth.

🔁 Stack of Layers

This process is repeated \(N\) times. Each layer refines the understanding.

• Early layers learn local syntax.
• Mid layers capture sentence structure.
• Later layers understand abstract meaning.

🛠️ Key Advantages of Transformer Architecture

Parallel Processing

• Unlike RNNs which process tokens one by one, transformers process all tokens simultaneously using self-attention.
• This massively improves training speed and allows efficient use of modern GPUs and TPUs.

Captures Long-Range Dependencies

• Self-attention computes pairwise relationships between all tokens.
• This makes it easy to model dependencies even between distant tokens in a sequence.
• In contrast, RNNs suffer from vanishing gradients over long sequences.

Scalable to Large Models

• Transformers scale well with increased model size and data.
• They’ve been used to train models with billions to trillions of parameters, such as GPT-4, PaLM, and Claude.

Supports Pretraining + Finetuning

• Transformers excel in transfer learning:
  • Pretrain on massive, diverse corpora (e.g., web, books, code)
  • Finetune on specific downstream tasks with relatively small labeled datasets
• This is the foundation of modern LLM pipelines.

⚡ Limitations

Quadratic Attention Complexity

• Self-attention requires computing a matrix of size \(n \times n\) (where \(n\) = sequence length).
• Memory and compute costs scale quadratically: \(\mathcal{O}(n^2)\).
• Long documents are expensive to process.

Fixed Context Window

• Transformers have a maximum sequence length, often 2048–32K tokens (though some newer models go up to 128K).
• Input that exceeds this limit is truncated or must be chunked.

Resource-Intensive

• Training and running large transformers require significant memory, compute, and energy.
• Not ideal for real-time applications or deployment on low-resource devices.

Despite these limitations, transformers remain the dominant architecture in NLP and are being extended to vision, audio, robotics, and multimodal applications.

Why Models Like GPT (Decoder-Only) Can Do Translation, Summarization, Multimodal

Although GPT is a decoder-only transformer, it can handle tasks traditionally associated with encoder–decoder models because of how it’s trained and how prompting works:

1. Instruction Tuning

GPT models are trained on datasets that include examples of translation, summarization, Q&A, etc. These are framed as text-in → text-out tasks.

• Example: “Translate English to French: The cat sleeps →”
• The prompt encodes both the task type and input context.

2. Unified Text Format

In decoder-only transformers:

• Inputs and outputs are handled in the same sequence stream.
• The model learns to complete tasks based on pattern recognition from massive pretraining + finetuning.
• The autoregressive setup means GPT is great at generating structured completions.

3. Prompt Engineering

You can turn nearly any problem into a single text string, which GPT learns to respond to appropriately:

Summarize: Climate change is accelerating due to... → Summary:
Translate: Hello, how are you? → French:
Describe this image: <image tokens> → A dog jumping over a fence.

This lets GPT solve problems without separate encoder/decoder modules.

4. Multimodal Support via Tokenization

• In GPT-4 and other multimodal variants, non-text data (like images) are tokenized and embedded into the same input stream.
• This allows models to reason over images + text together, using the same decoder-only architecture.

Why Encoder-Only and Full Transformer Models Are Still Valuable

Encoder-Only Models (like BERT)

• Produce rich contextual embeddings for each token.
• Best used when no generation is needed, e.g.:
  • Classification (e.g. sentiment analysis)
  • Semantic similarity
  • Named entity recognition
• They’re bidirectional — can attend to both left and right context.

Encoder–Decoder Models (like T5, BART)

• Best for structured input–output mappings like:
  • Translation
  • Summarization
  • Question answering
• Clear separation of input encoding and output decoding allows better alignment in sequence-to-sequence problems.
• The encoder provides full comprehension of input, while the decoder generates target sequences with cross-attention.

✅ Final Thoughts

Decoder-only transformers like GPT have proven incredibly powerful, as they can perform many tasks just by clever prompting, without needing a full encoder-decoder structure.

Still, encoder-only and full transformer models are valuable in understanding tasks and structured input-output tasks, respectively.

The choice depends on task structure and deployment goals.

Understanding these architectures is essential to mastering LLMs like GPT, BERT, Claude, Gemini, LLaMA, and beyond.

For further inquiries or collaboration, feel free to contact me at my email.