Inside the Transformer: How the Architecture Behind ChatGPT Actually Works

01The Problem Transformers Solved: Limitations of RNNs and LSTMs

Before the transformer, sequence processing belonged to recurrent neural networks. An RNN reads input one token at a time, carrying a hidden state forward from each step to the next. This design is intuitive but it comes with a fundamental constraint: information from the start of a long sequence has to survive through every intermediate step to remain available at the end.

In practice, that survival is unreliable. Gradients flowing backward through many time steps either vanish into insignificance or explode into instability, a problem known as the vanishing and exploding gradient problem. Long Short-Term Memory networks, or LSTMs, mitigated this with gating mechanisms that learn what to keep and what to discard, but they did not remove the underlying sequential bottleneck. Every token still had to wait for the previous one.

The practical consequence was twofold. Training was slow, because there was no way to parallelize across the sequence dimension. And long-range dependencies were hard to learn, because the model had to encode everything into a fixed-size hidden state that was repeatedly overwritten. The transformer, introduced in 2017, attacked both problems at once by discarding recurrence entirely and letting every token attend directly to every other token in a single parallel operation.

02Self-Attention: The Core Innovation

Self-attention is the mechanism that replaced recurrence. Instead of processing tokens one at a time, it computes a relationship between every pair of tokens in the sequence simultaneously. The intuition is simple: to understand a word, look at all the other words in the sentence and decide which ones are most relevant.

Concretely, each token is projected into three vectors called query, key, and value. The query represents what the token is looking for, the key represents what each token has to offer, and the value represents the content that gets passed along. Attention scores are computed by taking the dot product of a query with every key, scaling the result, applying a softmax to turn it into a set of weights, and then using those weights to combine the values into an output.

The elegance of this formulation is that it is a single set of matrix multiplications. The entire sequence is processed in parallel on a GPU, and every token can gather context from every other token in one shot. There is no hidden state being passed forward, no waiting for the previous step. The cost is quadratic in sequence length, because every token attends to every other, but for the sequence lengths that matter in practice, the parallelism wins decisively.

03Multi-Head Attention and Why Multiple Heads Matter

A single attention computation captures one pattern of relationship between tokens. But language contains many kinds of relationship at once: syntax, coreference, semantic similarity, and positional patterns all coexist in the same sentence. Multi-head attention addresses this by running the attention mechanism in parallel multiple times, each with its own learned query, key, and value projections.

Each head can specialize in a different kind of relationship. One head might learn to connect pronouns to their antecedents. Another might track subject-verb agreement. A third might attend to the tokens that answer a question. The outputs of all heads are concatenated and linearly projected back into the model dimension, merging the different perspectives into a single representation.

The number of heads is a design choice that trades specialization against per-head capacity. Too few heads and the model cannot capture the full range of relationships. Too many and each head becomes too small to learn anything meaningful. In practice, large models use dozens of heads, and the pattern they learn is remarkably consistent: early layers capture local syntax, middle layers capture semantic relationships, and later layers integrate information across long distances. The chart below shows how the parameter counts of models using this architecture have grown over time.

04Positional Encoding: How Transformers Understand Word Order

Self-attention is permutation-invariant by design. If you shuffle the input tokens, the attention mechanism produces the same set of relationships, just reordered. This is a problem, because word order matters enormously in language. "The dog bit the man" and "The man bit the dog" contain the same words but mean very different things.

The solution is positional encoding, a set of vectors added to the input embeddings that give each position a unique signature. The original transformer used sinusoidal encodings of different frequencies, so that each position gets a distinctive pattern and the model can learn to use relative positions through simple linear transformations of these patterns.

Later models have explored alternatives. Learned positional embeddings, where each position gets its own trainable vector, became common in models like BERT and GPT. Rotary position embeddings, which rotate the query and key vectors by a position-dependent angle, have become the dominant choice in recent open-weight models because they generalize better to sequence lengths not seen during training. The unifying principle is the same: inject position information into the representation so that attention can reason about order even though the mechanism itself is order-agnostic.

05The Encoder-Decoder Structure and How It Processes Language

The original transformer had two halves: an encoder and a decoder. The encoder reads the full input sequence and produces a set of context-rich representations. The decoder generates the output sequence one token at a time, attending both to its own previously generated tokens and to the encoder outputs through a cross-attention layer.

This design was built for sequence-to-sequence tasks like translation, where the input and output are in different languages and the model needs a full understanding of the source before it can begin producing the target. The encoder-decoder split made that natural: understand first, then generate.

Not every model uses both halves. BERT is encoder-only, designed to produce rich bidirectional representations for understanding tasks like classification and question answering. GPT and its successors are decoder-only, using a masked self-attention that prevents each token from seeing future positions, making the model naturally suited to autoregressive generation. Despite this architectural divergence, all three share the same core ingredients: multi-head self-attention, positional encoding, feed-forward layers, residual connections, and layer normalization. The transformer is less a single architecture than a family of architectures built from the same parts.

06From Transformers to LLMs: How GPT, Claude, and Gemini Use This Architecture

Every major large language model in 2026 is a transformer at its core, but each has made distinct engineering choices within that family. GPT models are decoder-only, trained to predict the next token across vast text corpora, and then refined with instruction tuning and reinforcement learning from human feedback. Claude follows a similar decoder-only philosophy with differences in training methodology, alignment techniques, and context handling. Gemini extends the transformer paradigm to be natively multimodal, processing text, images, and audio within a unified architecture.

What separates these models is not the attention mechanism itself but everything around it: the scale of training data, the quality of the post-training pipeline, the context window length, the mixture-of-experts routing strategies that let some models activate only a fraction of their parameters for any given token, and the infrastructure for inference. The transformer is the foundation; these engineering choices are the building.

The result is that the same architectural sketch, attention over token sequences, has produced systems that write code, analyze images, reason through multi-step problems, and hold extended conversations. The diversity of capability comes not from abandoning the transformer but from pushing every dimension of it further: more parameters, more data, more compute, and more sophisticated training objectives.

07Scaling Laws and the Future of Transformer-Based Models

The most consequential finding of the last several years is that transformer performance follows remarkably smooth scaling laws. Given more parameters, more data, and more compute, loss decreases in a predictable power-law relationship. This is not a coincidence of any one model; it is a regularity that has held across labs, datasets, and architectural variants. The chart below illustrates the trajectory of training compute, which has grown by orders of magnitude as labs chase the next increment of capability.

The implication is double-edged. On one hand, scaling works, and the recipe for better models is in large part the recipe for more compute and better data. On the other hand, the cost is growing, the energy demands are becoming a serious concern, and there are open questions about how long the power-law relationship holds before diminishing returns or data scarcity intervene.

Research is already branching in several directions. Mixture-of-experts architectures increase parameter count without proportionally increasing compute per token. Efficient attention variants reduce the quadratic cost of self-attention for long sequences. New architectures that are not strictly transformers, but borrow their principles, are being explored. Whether the next dominant architecture is a transformer or its successor, the ideas that the transformer introduced, parallel attention over token relationships, learned positional reasoning, and the scaling hypothesis, are now the baseline that everything else is measured against.