Transformer Architecture Explained: Components and Step-by-Step Process

Transformer architecture is the design behind almost every major AI model you use today, including GPT, Claude, Gemini, and Llama. It replaced older models that read text one word at a time.

The big idea is simple. A transformer looks at every word in a sentence at once. Then it works out which words matter most to each other. That one change made AI faster to train and much better at understanding what people actually mean.

This guide from SolGuruz explains transformer architecture step by step. You will learn what each piece does and how data moves through the model. You will also see the three main types, the key limits, and which build path makes sense for your product.

Transformer architecture is an AI model design that helps machines understand how words in a sentence relate to each other and what they mean in context. It reads all the words at once and uses a method called self-attention to decide which words matter most. Most large language models (LLMs) today run on it.

Think of it like this. When you read a sentence, you don’t understand each word on its own. You look at the words around it to get the full meaning. A transformer does the same thing.

For example, take this sentence: “The cat sat on the mat because it was tired.” The transformer looks at the nearby words and works out that “it” means the cat, not the mat. Now change one word: “The cat sat on the mat because it was soft.” This time, the model links “it” to the mat, because a mat can be soft.

Here is the quick picture in three simple steps:

1. Reads the input: It takes in text, such as a question, an email, or a paragraph.
2. Finds relationships: It uses attention to spot which words help explain other words.
3. Builds the meaning: It creates a context-aware version of the text, which helps the AI answer questions, write summaries, or translate languages.

Today, businesses use this same design to build chatbots, smart search tools, and writing assistants. Planning to build one? Our custom LLM development services for business applications can help you pick the right model and launch faster.

Google researchers introduced this deep learning design in the 2017 paper Attention Is All You Need. They first built it for language translation. The larger version had 213 million parameters and was trained in 3.5 days on 8 GPUs. Parameters are the settings a model adjusts while it learns, a bit like the knobs on a sound mixer.

Before 2017, most natural language processing (NLP) models were recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). These models read text one word at a time, in order. Picture reading a book through a tiny keyhole that shows only one word at a time. 

That design caused two big problems. First, the models lost track of early words in long sentences. Think of a friend telling a long story: by the end, you may forget how it started. Plain RNNs lose early words because of the vanishing gradient problem. LSTMs reduce it but still struggle with very long text. 

Second, these models trained slowly because each word had to wait for the previous one. It works like a store with a single checkout counter. Meanwhile, the computer chips used for AI, called GPUs, are built to run many counters at once.

The transformer neural network largely solved both problems by reading all the words at once.

If your team still runs older sequence models in production, machine learning development services can help you judge which ones are worth upgrading.

A labeled transformer architecture diagram looks complex at first, but it follows a simple story. Think of it as two buildings standing side by side. The left building is the encoder, which understands the input. The right building is the decoder, which writes the output. Data enters on the ground floor and moves up.

Some steps repeat inside each building, so you will see a few numbers more than once in the diagram. Here is what each numbered block means:

1. Input and Output Embedding

The ground floor of each building, where each token becomes a list of numbers that holds its meaning.

2. Positional Encoding

A position tag added to each word, so the model knows the word order. The “+” means the tag is added on top of the word’s numbers.

3. Nx

A note that each building repeats its floors N times. In the original model, N was 6.

4. Multi-Head Attention

The floor where words compare themselves with one another to understand context.

5. Add & Norm

A step after every floor that adds the floor’s input back to its output and keeps the numbers steady.

6. Feed Forward

The floor where each word refines its own meaning.

7. Outputs (shifted right)

The words that enter the right building. During training, the decoder sees the correct answer moved one position right, so it learns to guess each next word. When the model is in use, these are the words it has written so far.

8. Masked Multi-Head Attention

The decoder’s first floor, which hides future words so the model cannot peek.

9. Linear and Softmax

The top floor of the right building, which turns the result into a probability for every word in the vocabulary. The decoding settings then pick the next word.

The arrow from the encoder into the middle of the decoder is called cross-attention. This is the decoder’s second attention floor, and it is how the decoder reads what the encoder understood.

Put simply, the encoder reads, the decoder writes, and cross-attention connects the two. For example, when you translate “Good morning” into Spanish, the encoder first builds a full picture of the English phrase. The decoder then uses that picture to write “Buenos días” one word at a time. Once this flow is clear, the other transformer designs covered below become much easier to follow.

A transformer is built from seven core parts. Each part is simple on its own. The real power comes from how they stack and work together.

1. Tokenization and Embeddings

Tokenization splits raw text into small pieces called tokens, and embeddings turn each token into numbers that hold its meaning. This step comes first because AI models cannot read words. They only understand numbers.

A token can be a full word, part of a word, or a single character. For example, “I love pizza” might become three tokens: “I,” “love,” and “pizza.” A longer word like “unhappiness” might split into “un,” “happi,” and “ness.”

An embedding is a list of numbers, called a vector, that stores the token’s semantic meaning. Think of it as an address on a map of meanings. Words with close meanings live near each other, so “invoice” and “bill” are neighbors, while “invoice” and “banana” are far apart. Search tools store these embeddings in a vector database to find matches by meaning.

2. Positional Encoding

Positional encoding adds a position signal to each token, so the model knows word order even while it reads everything at once. Without it, “dog bites man” and “man bites dog” would look the same to the model, even though they mean very different things.

It works like seat numbers in a cinema. The tag tells the model that “dog” sits in seat 1 and “man” sits in seat 3. Many newer models use rotary position embeddings (RoPE), which track the distance between words and work well with long text.

3. Self-Attention (Query, Key, and Value)

Self-attention is the attention mechanism at the heart of transformer architecture. It lets every word look at every other word and decide how much each one matters to its meaning.

For example, take “The trophy didn’t fit in the bag because it was too big.” Attention links “it” to “trophy.” After all, a trophy that is too big would not fit in a bag.

To do this, the model gives each word three roles. A library search is an easy way to picture them:

• Query (Q): The question a word asks, like a search you type into a library system.
• Key (K): The label each word offers, like the title on a book’s spine.
• Value (V): The actual content a word shares, like the information inside the book.

The model matches each query with every key using a math step called a dot product. Strong matches get high scores. A function called softmax then turns those scores into percentages that add up to 100%. In our example, “it” might give 80% of its attention to “trophy,” 15% to “bag,” and 5% to the other words.

4. Multi-Head Attention

Multi-head attention runs several attention passes side by side, so the model can track different word relationships at the same time. One pass alone can notice only one kind of link.

Imagine a team of editors reading the same sentence. One checks grammar. Another tracks who did what. A third notices the tone. Then they combine their notes into one clear picture. The base model in the original paper used 8 of these “heads” and the larger one used 16, while large modern models use dozens. 

5. Feed-Forward Network

A feed-forward network refines each word on its own after attention has mixed information between words. It helps the model spot deeper patterns.

If attention is a group discussion, the feed-forward step is quiet thinking time afterward. Each word takes what it learned from the group and sharpens its own meaning. Research also suggests these layers store many facts a model learns. One example is “Paris is the capital of France.”

6. Residual Connections and Layer Normalization

Residual connections and layer normalization keep training stable, so models can stack many layers without losing information. Every attention step and feed-forward step comes with both helpers.

A residual connection adds the step’s original input back to its output. It works like keeping your first draft next to the edited version, so nothing important gets lost. Layer normalization then keeps the numbers in a steady range. It acts like a volume limiter that stops music from getting too loud or too quiet.

7. Encoder and Decoder Stacks

Both the encoder and the decoder are stacks of identical layers placed one on top of another. Each layer builds on the work of the one below it. This depth is what lets a transformer move from reading single words to understanding full ideas.

Picture a photo slowly coming into focus. The first layers outline basic shapes, and later layers fill in the fine details. Research suggests transformer layers work in a similar way: early layers pick up simple patterns, while later layers capture context and intent. That is also why models are often described by layer count. BERT, for example, comes in 12-layer and 24-layer versions.

Now let’s follow one sentence through the full model. Here is how a transformer turns “Our app is ready” into French.

Step 1: Split the text

The tokenizer breaks the sentence into tokens: “Our,” “app,” “is,” and “ready.”

Step 2: Turn words into numbers

Each token becomes an embedding that stores its meaning.

Step 3: Add word order

Positional encoding marks “Our” as first, “app” as second, and so on.

Step 4: Understand the sentence

The encoder runs 6 layers of attention and feed-forward steps. By the end, every word knows its role in the full sentence.

Step 5: Start writing

The decoder begins with a special start signal. Masked attention stops it from seeing future words, and cross-attention lets it read the encoder’s notes.

Step 6: Pick the first word

The linear and softmax layer scores every word in the vocabulary and turns those scores into probabilities. The model then picks the most likely word, “Notre.” 

Step 7: Repeat until done

The new word goes back into the decoder, which then picks “application,” “est,” “prête,” and finally a stop signal.

For example, if you type “The best time to visit Japan is,” the model may predict “spring,” then “because,” then “of,” one word at a time. A setting called temperature controls each pick. Low temperature chooses the safest word, while high temperature allows more surprising choices.

Not every task needs both halves of the model. So researchers split the design into three main types. An easy way to remember them: encoder-only models are readers, decoder-only models are writers, and encoder-decoder models are translators.

Decoder-only transformer architecture leads today because one design can handle chat, writing, and coding when trained at scale. Meanwhile, encoder-only models still win for fast, low-cost jobs like search ranking and spam filtering. If your product writes content or answers questions, a decoder-only model is the usual starting point. A generative AI development company for custom AI products can help you choose the right one.

Vision Transformer Architecture

Transformers also work beyond text. In 2020, Google researchers introduced the Vision Transformer (ViT), which brought the same design to computer vision. It cuts an image into small squares, often 16 by 16 pixels, and treats each square like a word.

For example, a photo of a dog becomes a grid of small squares. Attention helps the model see that the square with an ear belongs with the square with an eye, even when they sit far apart. Diffusion transformers use a similar idea to create images and videos, and the same design now powers speech recognition too. 

The core idea from 2017 has held up well, but LLM builders have tuned almost every part for speed and scale. These upgrades shape how transformer architecture in large language models works today, from reading long documents to running at a lower cost.

For example, the KV cache works like sticky notes while reading a long book. Instead of rereading every page before each new sentence, the model checks its notes and moves on.

Scale has grown too. GPT-3 used 96 layers and 175 billion parameters, compared with the original model’s 6 encoder and 6 decoder layers. Today, these models also use tools and work together in chains. That shift is the basis for the different types of AI agents businesses now deploy.

Transformers are powerful, but they come with real trade-offs. Here is what most AI vendors skip when they pitch a new feature.

1. Attention cost grows fast with input length

Every token checks every other token. So a 1,000-token document needs about 1 million comparisons, while a 2,000-token document needs about 4 million.

2. Context windows have a hard limit

A model can read only a set number of tokens at once. It is like a whiteboard of fixed size: once it fills up, older notes get wiped unless you add retrieval or memory. This is the exact problem context engineering solves. 

3. Compute and memory needs are heavy

Training a large model takes thousands of GPUs, and running one still needs strong hardware. Most teams rent this power through cloud APIs instead. Teams that choose to run an LLM locally on their own servers usually do it for privacy and data control, and they plan for the hardware cost upfront. 

4. Confident mistakes happen

Transformers predict likely text, not checked facts, so they can give wrong answers that sound right. These errors are called AI hallucinations. 

5. Answers come out one word at a time

A decoder must finish each word before it can start the next one. That is why long answers stream in slowly, and why response speed matters when you design an AI feature. 

Researchers are testing alternatives such as state space models, and some new models mix both approaches. For now, though, transformers remain the default choice for language AI. 

Once you understand how the architecture works, a practical question follows. How should your business actually use it? The good news is that most teams never need to train a transformer from scratch.

Running these models has also become far cheaper. According to the Stanford AI Index 2025, using a model as capable as GPT-3.5 became more than 280 times cheaper. That drop happened between November 2022 and October 2024. In our experience, the best first project is narrow and repetitive, such as sorting support tickets. Results there are easy to measure, and AI integration services for existing software can connect the model to the helpdesk or CRM your team already uses.

Getting a car is a simple way to compare them. Using a hosted model is like renting one. Adding retrieval is like renting one and loading your own maps. Fine-tuning an LLM is like customizing a car you bought. Training from scratch is like building your own car factory. 

A sensible path is to start with better prompts, add retrieval when answers need company data, and fine-tune only when testing shows a clear gap. Our wiki covers the first two steps in detail: prompt engineering and retrieval-augmented generation (RAG).

Transformer architecture changed AI by replacing word-by-word reading with self-attention. That single shift made models faster to train, better at context, and able to grow to billions of parameters. Every major LLM today builds on the same parts: embeddings, positional encoding, attention, feed-forward layers, and stacked blocks.

To see how these ideas connect to RAG, agents, and fine-tuning, browse the rest of the SolGuruz generative AI wiki. Planning a product built on transformer models? Talk to our AI engineers about the right model and build path.

1. What is a transformer model in AI?

A transformer model is an AI model built on transformer architecture. It reads a full input at once and uses self-attention to find which words relate. In generative AI, it powers chatbots and writing tools.

2. When was the transformer introduced, and who created it?

Eight Google researchers, including first author Ashish Vaswani, introduced the transformer in June 2017 in the paper “Attention Is All You Need.” They first designed it to improve machine translation.

3. Is ChatGPT based on a transformer?

Yes. ChatGPT runs on OpenAI’s GPT models, and GPT stands for Generative Pre-trained Transformer. These are decoder-only models that predict text one word at a time.

4. What is self-attention in transformers?

Self-attention lets each word check every other word to find what matters most. For example, in “The server crashed because it ran out of memory,” it links “it” to “server.” 

5. What is the difference between an encoder and a decoder?

The encoder reads and understands the input. The decoder uses that understanding to write the output one word at a time, without seeing future words.

6. How is a transformer different from an RNN or LSTM?

RNNs and LSTMs read words one by one, so they train slowly and struggle to hold early words in long text. Transformers read all words at once, which speeds up training and keeps long-range context. 

7. How many layers does a transformer model have?

The original model stacked 6 encoder and 6 decoder layers. Modern models go much deeper. GPT-3, for example, uses 96 layers and 175 billion parameters.

8. What kind of transformer model is BERT?

BERT is an encoder-only transformer model released by Google in 2018. It reads text in both directions at once, which makes it strong at search, classification, and sentiment tasks rather than writing text.

9. What is a context window in a transformer?

A context window is the maximum number of tokens a model can read at once. The model ignores anything beyond it unless you add retrieval or memory systems.

10. Is a transformer a type of neural network?

Yes. A transformer is a type of neural network, just like RNNs and LSTMs. What makes it different is self-attention, which lets it read all words at once instead of one at a time.

11. Are transformers and LLMs the same thing?

No. A transformer is the model design, while an LLM is a large model trained on huge amounts of text. Almost every modern LLM uses a decoder-only transformer design.