How GPT Works: Transformers Explained for Everyone (2026)

What’s actually happening when ChatGPT writes a poem, debugs your code, or explains quantum physics in the style of a pirate? It’s easy to think of it as magic—or worse, to imagine there’s some vast database of pre-written responses it’s searching through.

The reality is both simpler and more fascinating. GPT and similar models are sophisticated text prediction machines built on an architecture called the Transformer. They don’t “know” things in the human sense; they’ve learned statistical patterns from enormous amounts of text and use those patterns to generate remarkably coherent language.

I’ve found that understanding how GPT works—even at a conceptual level—changes how you use it. You develop intuitions about what it’s good at, why it sometimes fails, and how to get better results. So let’s peek under the hood.

This won’t be a computer science lecture. I’m going to explain transformers the way I’d explain them to a curious friend: with analogies, without equations, and with honesty about what we do and don’t understand about why they work so well.

What Does “GPT” Actually Mean?

Let’s start with the name. GPT stands for three things, each describing a core aspect of how the technology works:

G - Generative: The model generates new content. It doesn’t retrieve pre-written responses from a database. When you ask ChatGPT a question, it creates the answer from scratch, one piece at a time. That’s why you can ask the same question twice and get different responses—and why it can write about topics that didn’t exist when it was trained.

P - Pre-trained: Before you ever use it, GPT has already been trained on enormous amounts of text—we’re talking significant portions of the internet, millions of books, code repositories, and more. This pre-training process is where it learns language patterns, facts (though imperfectly), and writing styles. The “pre” matters because the heavy lifting happens before deployment.

T - Transformer: This is the neural network architecture that makes it all work. The transformer is the breakthrough innovation that enabled modern AI language models. It’s what we’re mainly going to explore in this guide.

If you want the broader picture of what large language models are and how they fit into AI more generally, check out our LLM explainer.

The Transformer: A Brief History

The transformer architecture was introduced in a 2017 paper with the memorable title “Attention Is All You Need.” Published by researchers at Google, it fundamentally changed how machines process language.

Before transformers, the dominant approach for language tasks was something called recurrent neural networks (RNNs) and their variants like LSTMs. These processed text sequentially—word by word, in order. Imagine reading a book by looking at one word at a time, remembering what you’d read before, and predicting what comes next.

This worked, but it had problems. Sequential processing is slow (you can’t parallelize easily). More critically, information from earlier in a sequence tends to get “diluted” by the time you reach the end. If a sentence starts with “The cat that chased the mouse that scared the elephant across the room” and ends with “is hungry,” RNNs struggled to connect “is hungry” back to “The cat.”

Transformers solved this by processing the entire sequence at once and using “attention” to directly connect any part of the input to any other part. The cat can directly “attend to” the word “hungry” regardless of how many words separate them.

This was revolutionary. Suddenly, models could:

Process much longer texts effectively
Train much faster (parallel processing)
Capture long-range dependencies that RNNs missed

GPT specifically uses a “decoder-only” transformer architecture—meaning it’s designed for generating text rather than analyzing it. But the core innovation—attention—is what powers it all.

How GPT Processes Text (Step by Step)

Let’s walk through what actually happens when you send a message to GPT. The process has several stages, each building on the last.

Step 1: Tokenization (Breaking Text into Pieces)

Your input text first gets broken into smaller pieces called tokens. These aren’t exactly words—they’re more like word-pieces. Common words might be single tokens, while unusual words get split up.

For example:

“The” → 1 token
“cat” → 1 token
“tokenization” → multiple tokens (maybe “token”, “ization”)
“supercalifragilisticexpialidocious” → many tokens

The model works with a vocabulary of about 100,000 tokens. Anything you write gets mapped to sequences of these tokens before processing begins.

For a deep dive into how tokenization works and why it matters, see our tokens guide.

Step 2: Converting to Numbers (Embeddings)

Computers work with numbers, not words. Each token gets converted into a list of numbers called an “embedding”—typically around 1,000-4,000 numbers, depending on the model.

Here’s the key insight: similar words have similar embeddings. The numbers for “cat” and “kitten” are closer together than “cat” and “refrigerator.” The embedding captures something about the meaning of the token.

These aren’t hand-coded; the model learns them during training. It discovers that “king” and “queen” should be similar in certain ways but different in others, all encoded in these numerical vectors. For a visual explanation of these concepts, Jay Alammar’s Illustrated Transformer is an excellent resource.

Step 3: Adding Position Information (Positional Encoding)

Here’s a problem: if we’re processing all tokens at once (not sequentially), how does the model know their order? “The dog chased the cat” and “The cat chased the dog” have the same tokens but very different meanings.

The solution is positional encoding. We add additional numbers to each token’s embedding that encode its position in the sequence. Position 1 gets one pattern, position 2 gets another, and so on. Now the model knows that “dog” comes before “chased” in the first sentence but after it in the second.

Step 4: The Attention Mechanism (The Key Innovation)

This is the heart of the transformer. The attention mechanism lets each token “look at” every other token and decide how much attention to pay to each.

Let me use an analogy. Imagine you’re reading this sentence: “The bank was closed, so I couldn’t deposit my check.”

When you process the word “bank,” your brain automatically considers context. The word “deposit” and “check” later in the sentence tell you this is a financial bank, not a river bank. You’re attending to relevant words elsewhere in the sentence.

That’s what attention does computationally. For each token, the model calculates “attention scores” with every other token. High scores mean “pay attention to this word when processing the current one.” Low scores mean “this word isn’t relevant right now.”

Mathematically, this involves queries, keys, and values—but conceptually, it’s:

For each word, ask “which other words should I focus on?”
Calculate relevance scores for all word pairs
Use those scores to create a weighted understanding of the whole sequence

The result: each token’s representation now includes information from relevant context throughout the entire sequence. “Bank” now “knows” about “deposit” and “check.”

Step 5: Multi-Head Attention (Multiple Perspectives)

One attention mechanism captures one type of relationship. But language has many types of relationships: grammatical, semantic, referential, stylistic.

Multi-head attention runs multiple attention mechanisms in parallel—typically 8 to 96 “heads” in modern models. Each head can specialize in different relationship types:

One head might track pronouns and their referents
Another might focus on subject-verb relationships
Another might capture semantic similarity

Think of it like having multiple people read the same text, each focusing on different aspects, then combining their insights.

Step 6: Building Understanding (Stacked Layers)

A single round of attention helps, but deep understanding requires multiple rounds. GPT models stack many transformer blocks—GPT-4 has over 100 layers.

Each layer refines the understanding from the previous one. Early layers might capture basic syntax. Middle layers might identify entities and relationships. Later layers might understand higher-level concepts and nuances.

It’s like reading a complex text multiple times. Each read deepens your understanding, revealing connections you missed before.

After passing through all these layers, each token’s representation has been transformed from a simple word embedding into a rich representation that captures its meaning in the full context of the input.

How GPT Generates Text

Understanding the input is only half the story. GPT also needs to generate output. This happens one token at a time, in a process called autoregressive generation.

Here’s how it works:

Process the input: All the tokenization, embedding, attention, and layer processing happens on your prompt.
Predict the next token: Based on that processed representation, the model outputs a probability distribution over all possible next tokens. “The” might have 0.1% probability, “cat” might have 3%, etc.
Sample from the distribution: The model picks a token based on these probabilities. This is where temperature comes in—higher temperature means more randomness (creative but potentially weird), lower temperature means sticking to the most probable tokens (predictable but safe).
Add that token and repeat: The chosen token becomes part of the sequence. Now we have the original input plus one new token. Process everything again and predict the next token.
Continue until done: Keep going until the model outputs a stop token, hits a length limit, or completes the response.

This is why generation is much slower than understanding—we have to run the full model once for each output token. A 1,000-word response means approximately 1,300 forward passes through the entire model.

It’s also why AI responses have a certain flow to them. Each word is predicted based on everything before it. The model is always asking “given everything so far, what’s most likely next?”

What Makes Transformers Different from Earlier AI

To appreciate why transformers were revolutionary, consider what came before.

Recurrent Neural Networks (RNNs): Processed text sequentially. Information from early in a sequence had to pass through every subsequent step to reach the end, leading to “vanishing gradients” where early context got lost.

Convolutional Neural Networks (CNNs): Could process in parallel but had limited receptive fields. They could only see a fixed window of surrounding words, missing long-range dependencies.

Transformers solved both problems:

Process the entire sequence in parallel (fast training)
Direct attention between any two tokens (no information loss over distance)
Scalable (throw more data and compute at them, they improve predictably)

The attention mechanism was the key. Instead of information having to flow step-by-step through a sequence, any token can directly attend to any other token. It’s the difference between a telephone game (each person whispers to the next) and a group conversation (anyone can address anyone).

This architecture turned out to scale remarkably well. Bigger models with more data consistently performed better—a property called “scaling laws” that has driven the race to build ever-larger models.

GPT vs Other Transformer Models

GPT isn’t the only way to use transformers. Different architectures suit different tasks:

GPT (Decoder-only): Designed for generation. Each token can only attend to tokens that came before it (causal attention). This makes sense for text generation—you can’t look at words you haven’t written yet. GPT, Claude, and LLaMA all use this architecture.

BERT (Encoder-only): Designed for understanding. Each token can attend to all other tokens (bidirectional attention). Great for tasks like classification, sentiment analysis, and question answering where you have the full text upfront. Not designed for generation.

T5 and BART (Encoder-Decoder): Use both. An encoder processes the full input bidirectionally, then a decoder generates output. Good for translation, summarization, and tasks with distinct input and output.

For most people using AI chatbots, you’re interacting with decoder-only models like GPT. They’re optimized for the back-and-forth of conversation and content generation.

For a detailed comparison of different AI assistants and how they differ, see our model comparison guide.

The Limits of GPT’s Architecture

Understanding how GPT works also reveals its inherent limitations.

It predicts, not reasons. GPT predicts probable next tokens based on patterns in training data. It doesn’t “think” or “reason” in the human sense. When it seems to reason, it’s reproducing patterns of reasoning from its training. This is why it can fail badly on novel problems while excelling at familiar ones.

Hallucinations are inherent. Since GPT generates based on what’s probable (not what’s true), it confidently produces plausible-sounding text that’s completely wrong. There’s no internal “fact-checking” mechanism—only pattern matching.

Context windows limit memory. All the attention happens within a fixed window. If your conversation exceeds that limit, early context gets cut off. The model literally can’t “remember” what scrolled out of view.

For more on context limitations, see our context window guide.

No learning during conversation. GPT doesn’t update its weights based on your conversation. It doesn’t “learn” your preferences (unless the platform builds a separate memory system). Each conversation starts fresh from the same trained model.

Quadratic attention cost. The attention mechanism compares every token to every other token—O(n²) complexity. Doubling context length quadruples computation. This is why very long contexts are expensive and why there’s active research on more efficient attention mechanisms.

These aren’t bugs that will be fixed in GPT-6. They’re fundamental to the architecture. Future improvements might come from hybrid approaches, better training, or entirely new architectures—but current transformer-based GPT models have these constraints baked in.

Frequently Asked Questions

Is GPT “really” understanding language?

This is a philosophical question as much as a technical one. GPT is doing sophisticated statistical pattern matching. Whether that constitutes “understanding” depends on how you define the term. It produces outputs that demonstrate contextual awareness, can follow complex instructions, and generalizes to new situations—all markers we might associate with understanding. But it has no conscious experience, no intentions, and no true comprehension.

My view: it’s a mistake to treat GPT as either “mere statistics” or “truly intelligent.” It’s something new—impressively capable in ways that simulate understanding, while fundamentally different from human cognition.

How much data was GPT trained on?

OpenAI hasn’t disclosed exact training data for recent models, but estimates suggest:

GPT-3: ~45 TB of text data
GPT-4: Significantly more, including curated datasets
Training involves both internet text and licensed/curated content

The scale is hard to grasp—we’re talking about a substantial fraction of everything ever written on the internet, plus books, academic papers, and code repositories.

Can I train my own GPT?

Training a model from scratch at GPT-4’s scale would cost hundreds of millions of dollars and require specialized infrastructure few organizations have access to. However, you can:

Fine-tune existing open-source models (like LLaMA) on your own data
Build applications using GPT via APIs
Run smaller open-source models locally for experimentation

What’s the difference between GPT and ChatGPT?

GPT is the base model—the raw transformer trained to predict tokens. It’s powerful but not optimized for conversation. ChatGPT is GPT plus additional training (RLHF—Reinforcement Learning from Human Feedback) that teaches it to be helpful, harmless, and conversational. It’s also wrapped in an interface that manages conversation history and system prompts. Think of GPT as the engine and ChatGPT as the finished car.

Will transformers be replaced?

Maybe eventually, but not immediately. There’s active research on alternatives—state space models, retentive networks, and various hybrid architectures. Some show promise on specific metrics. But transformers have a massive head start: enormous investment in tooling, optimization, and understanding. Any replacement would need to be significantly better to justify switching. For now, expect continued improvements to the transformer architecture rather than wholesale replacement.

Making Sense of the Magic

Let’s recap the journey from your prompt to GPT’s response:

Your text gets tokenized into pieces the model can work with
Tokens become numerical embeddings that capture meaning
Positional encoding preserves word order
The attention mechanism lets words look at relevant context
Multi-head attention captures different relationship types
Stacked layers deepen understanding progressively
Finally, the model predicts tokens one at a time to generate a response

Understanding this pipeline demystifies what AI assistants are doing. They’re not magic, not conscious, not searching databases. They’re incredibly sophisticated pattern-completion engines that have learned language structure from unprecedented amounts of text.

This knowledge is practical. When GPT hallucinates, you know why—it’s completing patterns, not checking facts. When it loses context in long conversations, you understand the constraint. When it produces impressive text, you appreciate the feat of engineering that makes it possible.

Transformers turned out to be a remarkably effective architecture for language—perhaps even more effective than their creators anticipated. Whether they represent a stepping stone to even more capable AI or a fundamental building block that will persist matters less than understanding what we’re working with today.

Now you know what’s under the hood. Use that knowledge to work with these tools more effectively—understanding their strengths, respecting their limits, and appreciating the genuine innovation they represent.